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 "revisiting deep architectures for head motion prediction in 360 videos"):
  Abstract: Head motion prediction is an important problem with 360\degree\ videos, in particular to inform the streaming decisions. Various methods tackling this problem with deep neural networks have been proposed recently. In this article we first show the startling result that all such existing methods, which attempt to benefit both from the history of past positions and knowledge of the video content, perform worse than a simple no-motion baseline. We then propose an LSTM-based architecture which processes the positional information only. It is able to establish state-of-the-art performance and we consider it our position-only baseline. Through a thorough root cause analysis, we first show that the content can indeed inform the head position prediction for horizons longer than 2 to 3s, the trajectory inertia being predominant earlier. We also identify that a sequence-to-sequence auto-regressive framework is crucial to improve the prediction accuracy over longer prediction windows, and that a dedicated recurrent network handling the time series of positions is necessary to reach the performance of the position-only baseline in the early prediction steps. This allows to make the most of the positional information and ground-truth saliency. Finally we show how the level of noise in the estimated saliency impacts the architecture's performance, and we propose a new architecture establishing state-of-the-art performance with estimated saliency, supporting its assets with an ablation study.

Includes members:
  Member "spherical clustering of users navigating 360 degree content":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a taxonomy and dataset for 360 videos")
  Member "predicting head movement in panoramic video a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a fixation based 360 benchmark dataset for salient object detection")
  Member "the prefetch aggressiveness tradeoff in 360 video streaming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a taxonomy and dataset for 360 videos")
  Member "predicting gaze in egocentric video by learning task dependent attention transition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "rate utility optimized streaming of volumetric media for augmented reality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint communication and computational resource allocation for qoe driven point cloud video streaming")
  Member "predicting deeper into the future of semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks")
  Member "making 360 circ video watchable in 2d learning videography for click free viewing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "360 indoor towards learning real world objects in 360 deg indoor equirectangular images")

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.CV(Computer Vision and Pattern Recognition)