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: "human in the loop wireless communications machine learning and brain aware resource management"
  Abstract: Human-centric applications such as virtual reality and immersive gaming will be central to the future wireless networks. Common features of such services include: a) their dependence on the human user's behavior and state, and b) their need for more network resources compared to conventional cellular applications. To successfully deploy such applications over wireless and cellular systems, the network must be made cognizant of not only the quality-of-service (QoS) needs of the applications, but also of the perceptions of the human users on this QoS. In this paper, by explicitly modeling the limitations of the human brain, a concrete measure for the delay perception of human users in a wireless network is introduced. Then, a novel learning method, called probability distribution identification, is proposed to find a probabilistic model for this delay perception based on the brain features of a human user. The proposed learning method uses both supervised and unsupervised learning techniques to build a Gaussian mixture model of the human brain features. Given a model for the delay perception of the human brain, a novel brain-aware resource management algorithm based on Lyapunov optimization is proposed for allocating radio resources to human users while minimizing the transmit power and taking into account the reliability of both machine type devices and human users. The proposed algorithm is shown to have a low complexity. Moreover, a closed-form relationship between the reliability measure and wireless physical layer metrics of the network is derived. Simulation results using real data from actual human users show that a brain-aware approach can yield savings of up to 78% in power compared to the system

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"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "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 "patient centric hetnets powered by machine learning and big data analytics for 6g networks"):
  with members:
    - "using machine learning and big data analytics to prioritize outpatients in hetnets"
    - "service embedding in iot networks"
    - "big data analytics for wireless and wired network design a survey"
    - "patient centric cellular networks optimization using big data analytics"
    - "energy efficient iot virtualization framework with peer to peer networking and processing"
    - "optimized energy aware 5g network function virtualization"
    - "stacking classifiers for anti spam filtering of e mail"

Connected through hyperedge HE-3 (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"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "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-4 (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"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

Connected through hyperedge HE-5 (induced by source paper "federated echo state learning for minimizing breaks in presence in wireless virtual reality networks"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "computation scheduling for distributed machine learning with straggling workers"
    - "towards federated learning at scale system design"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "learning to communicate in uav aided wireless networks map based approaches"
    - "edge computing meets millimeter wave enabled vr paving the way to cutting the cord"
    - "analysis of memory capacity for deep echo state networks"

Connected through hyperedge HE-6 (induced by source paper "a survey on low latency towards 5g ran core network and caching solutions"):
  with members:
    - "stochastic optimization and control framework for 5g network slicing with effective isolation"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "r fft function split at ifft fft in unified lte cran and cable access network"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "joint user association power control and scheduling in multi cell 5g networks"
    - "fundamental limits on latency in transceiver cache aided hetnets"
    - "decentralized coded caching in wireless networks trade off between storage and latency"
    - "provisioning low latency resilient mobile edge clouds for 5g"

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)