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 based facial image compression with semantic fidelity metric"):
  Abstract: Abstract   Surveillance and security scenarios usually require high efficient facial image compression scheme for face recognition and identification. While either traditional general image codecs or special facial image compression schemes only heuristically refine codec separately according to face verification accuracy metric. We propose a Learning based Facial Image Compression (LFIC) framework with a novel Regionally Adaptive Pooling (RAP) module whose parameters can be automatically optimized according to gradient feedback from an integrated hybrid semantic fidelity metric, including a successfully exploration to apply Generative Adversarial Network (GAN) as metric directly in image compression scheme. The experimental results verify the framework’s efficiency by demonstrating performance improvement of 71.41%, 48.28% and 52.67% Bit Rate Saving separately over JPEG2000, WebP and neural network-based codecs under the same face verification accuracy distortion metric. We also evaluate LFIC’s superior performance gain compared with latest specific facial image codecs. Visual experiments also show some interesting insight on how LFIC can automatically capture the information in critical areas based on semantic distortion metrics for optimized compression, which is quite different from the heuristic way of optimization in traditional image compression algorithms.

Includes members:
  Member "learning for video compression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning based video coding a review and a case study")
  Member "conditional probability models for deep image compression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical auto regressive model for image compression incorporating object saliency and a deep perceptual loss")
  Member "an end to end compression framework based on convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning for video compression")
  Member "guetzli perceptually guided jpeg encoder":
    also connected through other hyperedges:
      (none)
  Member "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to structure an image with few colors")
  Member "learning to inpaint for image compression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning for video compression")
  Member "learning convolutional networks for content weighted image compression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")

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.MM(Multimedia)