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: "optimal multicast of tiled 360 vr video in ofdma systems"
  Abstract: In this letter, we study optimal multicast of tiled 360 virtual reality (VR) video from one server (base station or access point) to multiple users in an orthogonal frequency division multiple access (OFDMA) system. For given video quality, we optimize the subcarrier, transmission power and transmission rate allocation to minimize the total transmission power. For given transmission power budget, we optimize the subcarrier, transmission power and transmission rate allocation to maximize the received video quality. These two optimization problems are non-convex problems. We obtain a globally optimal closed-form solution and a near optimal solution of the two problems, separately, both revealing important design insights for multicast of tiled 360 VR video in OFDMA systems.

Connected through hyperedge HE-1 (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"
    - "communications caching and computing for mobile virtual reality modeling and tradeoff"
    - "a survey on mobile edge computing the communication perspective"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "adaptive 360 vr video streaming divide and conquer"

Connected through hyperedge HE-2 (induced by source paper "optimal transmission of multi quality tiled 360 vr video by exploiting multicast opportunities"):
  with members:
    - "optimal multicast of tiled 360 vr video"
    - "optimal multi quality multicast for 360 virtual reality video"

Connected through hyperedge HE-3 (induced by source paper "matching capabilities of prediction to communication and computing for proactive vr video streaming"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "optimal task scheduling in communication constrained mobile edge computing systems for wireless virtual reality"
    - "adaptive 360 vr video streaming divide and conquer"

Connected through hyperedge HE-4 (induced by source paper "optimal multi view video transmission in ofdma systems"):
  with members:
    - "optimal multi view video transmission in multiuser wireless networks by exploiting natural and view synthesis enabled multicast opportunities"

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)