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 "learning compressible 360 deg video isomers"):
  Abstract: Standard video encoders developed for conventional narrow field-of-view video are widely applied to 360{\deg} video as well, with reasonable results. However, while this approach commits arbitrarily to a projection of the spherical frames, we observe that some orientations of a 360{\deg} video, once projected, are more compressible than others. We introduce an approach to predict the sphere rotation that will yield the maximal compression rate. Given video clips in their original encoding, a convolutional neural network learns the association between a clip's visual content and its compressibility at different rotations of a cubemap projection. Given a novel video, our learning-based approach efficiently infers the most compressible direction in one shot, without repeated rendering and compression of the source video. We validate our idea on thousands of video clips and multiple popular video codecs. The results show that this untapped dimension of 360{\deg} compression has substantial potential--"good" rotations are typically 8-10% more compressible than bad ones, and our learning approach can predict them reliably 82% of the time.

Includes members:
  Member "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to structure an image with few colors")
  Member "convolutional networks for spherical signals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "powerset convolutional neural networks")
  Member "graph based classification of omnidirectional images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kernel transformer networks for compact spherical convolution")
  Member "learning convolutional networks for content weighted image compression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "near lossless ell_ infty constrained image decompression via deep neural network")
  Member "full resolution image compression with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "pano2vid automatic cinematography for watching 360 circ videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "visual question answering on 360 deg images")
  Member "variable rate image compression with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to structure an image with few colors")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)