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: "the prefetch aggressiveness tradeoff in 360 video streaming"
  Abstract: With 360° video, only a limited fraction of the full view is displayed at each point in time. This has prompted the design of streaming delivery techniques that allow alternative playback qualities to be delivered for each candidate viewing direction. However, while prefetching based on the user's expected viewing direction is best done close to playback deadlines, large buffers are needed to protect against shortfalls in future available bandwidth. This results in conflicting goals and an important prefetch aggressiveness tradeoff problem regarding how far ahead in time from the current play-point prefetching should be done. This paper presents the first characterization of this tradeoff. The main contributions include an empirical characterization of head movement behavior based on data from viewing sessions of four different categories of 360° video, an optimization-based comparison of the prefetch aggressiveness tradeoffs seen for these video categories, and a data-driven discussion of further optimizations, which include a novel system design that allows both tradeoff objectives to be targeted simultaneously. By qualitatively and quantitatively analyzing the above tradeoffs, we provide insights into how to best design tomorrow's delivery systems for 360° videos, allowing content providers to reduce bandwidth costs and improve users' playback experiences.

Connected through hyperedge HE-1 (induced by source paper "revisiting deep architectures for head motion prediction in 360 videos"):
  with members:
    - "spherical clustering of users navigating 360 degree content"
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "rate utility optimized streaming of volumetric media for augmented reality"
    - "predicting deeper into the future of semantic segmentation"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "on human motion prediction using recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "a taxonomy and dataset for 360 videos"):
  with members:
    - "spherical clustering of users navigating 360 degree content"
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "movie editing and cognitive event segmentation in virtual reality video"
    - "viewport adaptive navigable 360 degree video delivery"

Connected through hyperedge HE-3 (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:
    - "optimized data representation for interactive multiview navigation"
    - "adaptive 360 vr video streaming divide and conquer"
    - "in network view synthesis for interactive multiview video systems"

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)