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 "prediction communication and computing duration optimization for vr video streaming"):
  Abstract: Proactive tile-based video streaming can avoid motion-to-photon latency of wireless virtual reality (VR) by computing and delivering the predicted tiles to be requested before playback. All existing works either focus on the task of tile prediction or on the tasks of computing and communications, overlooking the facts that these successively executed tasks have to share the same duration to avoid the latency and the quality of experience (QoE) of proactive VR streaming depends on the worst performance of the three tasks. In this paper, we jointly optimize the duration of the observation window for predicting tiles and the durations for computing and transmitting the predicted tiles to maximize the QoE given arbitrary predictor and configured resources. We obtain the global optimal solution with closed-form expression by decomposing the formulated problem equivalently into two subproblems. With the optimized durations, we find a resource-limited region where the QoE can be improved effectively by configuring more resources, and a prediction-limited region where the QoE can be improved with a better predictor. Simulation results using three existing tile predictors with a real dataset demonstrate the gain of the joint optimization over the non-optimized counterparts.

Includes members:
  Member "predicting head movement in panoramic video a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a fixation based 360 benchmark dataset for salient object detection")
  Member "very long term field of view prediction for 360 degree video streaming":
    also connected through other hyperedges:
      (none)
  Member "optimal multicast of tiled 360 vr video in ofdma systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal transmission of multi quality tiled 360 vr video by exploiting multicast opportunities")
  Member "communications caching and computing for mobile virtual reality modeling and tradeoff":
    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 "a survey on mobile edge computing the communication perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "virtual reality over wireless networks quality of service model and learning based resource management":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "risk based optimization of virtual reality over terahertz reconfigurable intelligent surfaces")
  Member "adaptive 360 vr video streaming divide and conquer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "is deadline oblivious scheduling efficient for controlling real time traffic in cellular downlink systems")

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)