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: "adaptive 360 vr video streaming divide and conquer"
  Abstract: While traditional multimedia applications such as games and videos are still popular, there has been a significant interest in the recent years towards new 3D media such as 3D immersion and Virtual Reality (VR) applications, especially 360 VR videos. 360 VR video is an immersive spherical video where the user can look around during playback. Unfortunately, 360 VR videos are extremely bandwidth intensive, and therefore are difficult to stream at acceptable quality levels. In this paper, we propose an adaptive bandwidth-efficient 360 VR video streaming system using a divide and conquer approach. In our approach, we propose a dynamic view-aware adaptation technique to tackle the huge streaming bandwidth demands of 360 VR videos. We spatially divide the videos into multiple tiles while encoding and packaging, use MPEG-DASH SRD to describe the spatial relationship of tiles in the 360-degree space, and prioritize the tiles in the Field of View (FoV). In order to describe such tiled representations, we extend MPEG-DASH SRD to the 3D space of 360 VR videos. We spatially partition the underlying 3D mesh, and construct an efficient 3D geometry mesh called hexaface sphere to optimally represent a tiled 360 VR video in the 3D space. Our initial evaluation results report up to 72% bandwidth savings on 360 VR video streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.

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"
    - "optimal multicast of tiled 360 vr video in ofdma systems"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "is deadline oblivious scheduling efficient for controlling real time traffic in cellular downlink systems"):
  with members:
    - "timely throughput of heterogeneous wireless networks fundamental limits and algorithms"
    - "online advertisement optimization and stochastic networks"
    - "optimal scheduling for fair resource allocation in ad hoc networks with elastic and inelastic traffic"

Connected through hyperedge HE-3 (induced by source paper "slamcast large scale real time 3d reconstruction and streaming for immersive multi client live telepresence"):
  with members:
    - "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"
    - "a dynamic hash table for the gpu"
    - "viewport adaptive navigable 360 degree video delivery"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

Connected through hyperedge HE-4 (induced by source paper "pano optimizing 360 deg video streaming with a better understanding of quality perception"):
  with members:
    - "yolov3 an incremental improvement"
    - "viewport adaptive navigable 360 degree video delivery"
    - "bola near optimal bitrate adaptation for online videos"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-5 (induced by source paper "visual distortions in 360 degree videos"):
  with members:
    - "bridge the gap between vqa and human behavior on omnidirectional video a large scale dataset and a deep learning model"
    - "estimation of optimal encoding ladders for tiled 360 deg vr video in adaptive streaming systems"
    - "a rate adaptation algorithm for tile based 360 degree video streaming"
    - "streaming virtual reality content"

Connected through hyperedge HE-6 (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 multicast of tiled 360 vr video in ofdma systems"
    - "optimal task scheduling in communication constrained mobile edge computing systems for wireless virtual reality"

Connected through hyperedge HE-7 (induced by source paper "had you looked where i m looking cross user similarities in viewing behavior for 360 video and caching implications"):
  with members:
    - "the prefetch aggressiveness tradeoff in 360 video streaming"
    - "optimized data representation for interactive multiview navigation"
    - "in network view synthesis for interactive multiview video systems"

Connected through hyperedge HE-8 (induced by source paper "predictive scheduling for virtual reality"):
  with members:
    - "timely throughput optimal scheduling with prediction"

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.MM(Multimedia)