human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "virtual reality over wireless networks quality of service model and learning based resource management"
  Abstract: In this paper, the problem of resource management is studied for a network of wireless virtual reality (VR) users communicating over small cell networks (SCNs). In order to capture the VR users' quality-of-service (QoS) in SCNs, a novel VR model, based on multi-attribute utility theory, is proposed. This model jointly accounts for VR metrics such as tracking accuracy, processing delay, and transmission delay. In this model, the small base stations (SBSs) act as the VR control centers that collect the tracking information from VR users over the cellular uplink. Once this information is collected, the SBSs will then send the three dimensional images and accompanying surround stereo audio to the VR users over the downlink. Therefore, the resource allocation problem in VR wireless networks must jointly consider both the uplink and downlink. This problem is then formulated as a noncooperative game and a distributed algorithm based on the machine learning framework of echo state networks (ESNs) is proposed to find the solution of this game. The use of the proposed ESN algorithm enables the SBSs to predict the VR QoS of each SBS and guarantees the convergence to a mixed-strategy Nash equilibrium. The analytical result shows that each user's VR QoS jointly depends on both VR tracking accuracy and wireless resource allocation. Simulation results show that the proposed algorithm yields significant gains, in terms of total utility value of VR QoS, that reach up to 22.2% and 37.5%, respectively, compared to Q-learning and a baseline proportional fair algorithm. The results also show that the proposed algorithm has a faster convergence time than Q-learning and can guarantee low delays for VR services.

Connected through hyperedge HE-1 (induced by source paper "risk based optimization of virtual reality over terahertz reconfigurable intelligent surfaces"):
  with members:
    - "on the reliability of wireless virtual reality at terahertz thz frequencies"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "human in the loop wireless communications machine learning and brain aware resource management"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "software defined network controlled switching between millimeter wave and terahertz small cells"

Connected through hyperedge HE-2 (induced by source paper "prediction communication and computing duration optimization for vr video streaming"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "very long term field of view prediction for 360 degree video streaming"
    - "optimal multicast of tiled 360 vr video in ofdma systems"
    - "communications caching and computing for mobile virtual reality modeling and tradeoff"
    - "a survey on mobile edge computing the communication perspective"
    - "adaptive 360 vr video streaming divide and conquer"

Connected through hyperedge HE-3 (induced by source paper "deep learning for content based personalized viewport prediction of 360 degree vr videos"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

Connected through hyperedge HE-4 (induced by source paper "towards tactile internet in beyond 5g era recent advances current issues and future directions"):
  with members:
    - "emerging edge computing technologies for distributed iot systems"
    - "the effect of iot new features on security and privacy new threats existing solutions and challenges yet to be solved"
    - "ultrareliable and low latency communication techniques for tactile internet services"
    - "scalable intelligence enabled networking with traffic engineering in 5g scenarios for future audio visual tactile internet"
    - "distributed optimization for energy efficient fog computing in the tactile internet"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "robust multi path communications for uavs in the urban iot"

Connected through hyperedge HE-5 (induced by source paper "experienced deep reinforcement learning with generative adversarial networks gans for model free ultra reliable low latency communication"):
  with members:
    - "generative adversarial networks for distributed intrusion detection in the internet of things"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "joint user association and resource allocation optimization for ultra reliable low latency hetnets"
    - "federated learning for ultra reliable low latency v2v communications"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"

Connected through hyperedge HE-6 (induced by source paper "data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach"):
  with members:
    - "cache placement in two tier hetnets with limited storage capacity cache or buffer"
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "edge computing meets millimeter wave enabled vr paving the way to cutting the cord"
    - "human in the loop wireless communications machine learning and brain aware resource management"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "echo state transfer learning for data correlation aware resource allocation in wireless virtual reality"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"

Connected through hyperedge HE-7 (induced by source paper "optimized trajectory design in uav based cellular networks for 3d users a double q learning approach"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks"
    - "joint altitude beamwidth location and bandwidth optimization for uav enabled communications"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "uav enabled wireless power transfer trajectory design and energy optimization"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"

Connected through hyperedge HE-8 (induced by source paper "a vision of 6g wireless systems applications trends technologies and open research problems"):
  with members:
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "wireless network intelligence at the edge"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "propagation measurement system and approach at 140 ghz moving to 6g and above 100 ghz"
    - "human in the loop wireless communications machine learning and brain aware resource management"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)