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: "taming the latency in multi user vr 360 circ a qoe aware deep learning aided multicast framework"
  Abstract: Immersive virtual reality (VR) applications are known to require ultra-high data rate and low-latency for smooth operation. In this paper, we propose a proactive deep-learning aided joint scheduling and content quality adaptation scheme for multi-user VR field of view (FoV) wireless video streaming. Using a real VR head-tracking dataset, a deep recurrent neural network (DRNN) based on gated recurrent units (GRUs) is leveraged to obtain users' upcoming tiled FoV predictions. Subsequently, to exploit a physical layer FoV-centric millimeter wave (mmWave) multicast transmission, users are hierarchically clustered according to their predicted FoV similarity and location. We pose the problem as a quality admission maximization problem under tight latency constraints, and adopt the Lyapunov framework to model the problem of dynamically admitting and scheduling proactive and real-time high definition (HD) video chunk requests, corresponding to a tile in the FoV of a user cluster for a given video frame, while maintaining the system stability. After decoupling the problem into three subproblems, a matching theory game is proposed to solve the scheduling subproblem by associating chunk requests from clusters of users to mmWave small cell base stations (SBSs) for multicast transmission. Simulation results demonstrate the streaming quality gain and latency reduction brought by using the proposed scheme. It is shown that the prediction of FoV significantly improves the VR streaming experience using proactive scheduling of the video tiles in the users' future FoV. Moreover, multicasting significantly reduces the VR frame delay in a multi-user setting by applying content-reuse in clusters of users with highly overlapping FoVs.

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)