human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach"):
  Abstract: In this paper, the problem of wireless virtual reality (VR) resource management is investigated for a wireless VR network in which VR contents are sent by a cloud to cellular small base stations (SBSs). The SBSs will collect tracking data from the VR users, over the uplink, in order to generate the VR content and transmit it to the end-users using downlink cellular links. For this model, the data requested or transmitted by the users can exhibit correlation, since the VR users may engage in the same immersive virtual environment with different locations and orientations. As such, the proposed resource management framework can factor in such spatial data correlation, to better manage uplink and downlink traffic. This potential spatial data correlation can be factored into the resource allocation problem to reduce the traffic load in both uplink and downlink. In the downlink, the cloud can transmit 360 contents or specific visible contents that are extracted from the original 360 contents to the users according to the users' data correlation to reduce the backhaul traffic load. For uplink, each SBS can associate with the users that have similar tracking information so as to reduce the tracking data size. This data correlation-aware resource management problem is formulated as an optimization problem whose goal is to maximize the users' successful transmission probability, defined as the probability that the content transmission delay of each user satisfies an instantaneous VR delay target. To solve this problem, an echo state networks (ESNs) based transfer learning is introduced. By smartly transferring information on the SBS's utility, the proposed transfer-based ESN algorithm can quickly cope with changes in the wireless networking environment.

Includes members:
  Member "cache placement in two tier hetnets with limited storage capacity cache or buffer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint caching and resource allocation in d2d assisted wireless hetnet")
  Member "communication computing and caching for mobile vr delivery modeling and trade off":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bandwidth gain from mobile edge computing and caching in wireless multicast systems")
  Member "edge computing meets millimeter wave enabled vr paving the way to cutting the cord":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mobile communications computing and caching resources optimization for coded caching with device computing")
  Member "human in the loop wireless communications machine learning and brain aware resource management":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "risk based optimization of virtual reality over terahertz reconfigurable intelligent surfaces")
  Member "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligent resource slicing for embb and urllc coexistence in 5g and beyond a deep reinforcement learning based approach")
  Member "virtual reality over wireless networks quality of service model and learning based resource management":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "risk based optimization of virtual reality over terahertz reconfigurable intelligent surfaces")
  Member "echo state transfer learning for data correlation aware resource allocation in wireless virtual reality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "taming the latency in multi user vr 360 circ a qoe aware deep learning aided multicast framework")
  Member "proactive resource management for lte in unlicensed spectrum a deep learning perspective":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks")

Question: Please tell me which class the source paper 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)