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: "echo state transfer learning for data correlation aware resource allocation in wireless virtual reality"
  Abstract: In this paper, the problem of data correlation-aware resource management is studied for a network of wireless virtual reality (VR) users communicating over cloud-based small cell networks (SCNs). In the studied model, small base stations (SBSs) with limited computational resources act as VR control centers that collect the tracking information from VR users over the cellular uplink and send them to the VR users over the downlink. In such a setting, VR users may send or request correlated or similar data (panoramic images and tracking data). This potential spatial data correlation can be factored into the resource allocation problem to reduce the traffic load in both uplink and downlink. This VR resource allocation problem is formulated as a noncooperative game that allows jointly optimizing the computational and spectrum resources, while being cognizant of the data correlation. To solve this game, a transfer learning algorithm based on the machine learning framework of echo state networks (ESNs) is proposed. Unlike conventional reinforcement learning algorithms that must be executed each time the environment changes, the proposed algorithm can intelligently transfer information on the learned utility, across time, to rapidly adapt to environmental dynamics due to factors such as changes in the users' content or data correlation. Simulation results show that the proposed algorithm achieves up to 16.7% and 18.2% gains in terms of delay compared to the Q-learning with data correlation and Q-learning without data correlation. The results also show that the proposed algorithm has a faster convergence time than Q-learning and can guarantee low delays.

Connected through hyperedge HE-1 (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"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "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-2 (induced by source paper "taming the latency in multi user vr 360 circ a qoe aware deep learning aided multicast framework"):
  with members:
    - "had you looked where i m looking cross user similarities in viewing behavior for 360 video and caching implications"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "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"
    - "toward low latency and ultra reliable virtual reality"
    - "beyond wysiwyg sharing contextual sensing data through mmwave v2v communications"

Connected through hyperedge HE-3 (induced by source paper "energy efficient mobile edge computation offloading for applications with shared data"):
  with members:
    - "minimizing latency to support vr social interactions over wireless cellular systems via bandwidth allocation"
    - "a survey on mobile edge computing the communication perspective"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "energy efficient resource allocation for mobile edge computing based augmented reality applications"
    - "decentralized computation offloading game for mobile cloud computing"
    - "energy efficient mobile cloud computing powered by wireless energy transfer"

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)