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: "spherical clustering of users navigating 360 degree content"
  Abstract: textabstractIn Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on their common navigation patterns is a first direction to understand users behavior. However, classical clustering techniques fail in identifying this common paths, since they are usually focused on minimizing a simple distance metric. In this paper, we argue that minimizing the dis- tance metric does not necessarily guarantee to identify users that ex- perience similar navigation path in the VR domain. Therefore, we propose a graph-based method to identify clusters of users who are attending the same portion of the spherical content over time. The proposed solution takes into account the spherical geometry of the content and aims at clustering users based on the actual overlap of displayed content among users. Our method is tested on real VR user navigation patterns. Results show that our solution leads to clusters in which at least 85% of the content displayed by one user is shared among the other users belonging to the same cluster.

Connected through hyperedge HE-1 (induced by source paper "revisiting deep architectures for head motion prediction in 360 videos"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "the prefetch aggressiveness tradeoff in 360 video streaming"
    - "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:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "the prefetch aggressiveness tradeoff in 360 video streaming"
    - "movie editing and cognitive event segmentation in virtual reality video"
    - "viewport adaptive navigable 360 degree video delivery"

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)