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 "a deep image compression framework for face recognition"):
  Abstract: Face recognition technology has advanced rapidly and has been widely used in various applications. Due to the extremely huge amount of data of face images and the large computing resources required correspondingly in large-scale face recognition tasks, there is a requirement for a face image compression approach that is highly suitable for face recognition tasks. In this paper, we propose a deep convolutional autoencoder compression network for face recognition tasks. In the compression process, deep features are extracted from the original image by the convolutional neural networks to produce a compact representation of the original image, which is then encoded and saved by existing codec such as PNG. This compact representation is utilized by the reconstruction network to generate a reconstructed image of the original one. In order to improve the face recognition accuracy when the compression framework is used in a face recognition system, we combine this compression framework with a existing face recognition network for joint optimization. We test the proposed scheme and find that after joint training, the Labeled Faces in the Wild (LFW) dataset compressed by our compression framework has higher face verification accuracy than that compressed by JPEG2000, and is much higher than that compressed by JPEG.

Includes members:
  Member "generative adversarial networks for extreme learned image compression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to structure an image with few colors")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "normface l 2 hypersphere embedding for face verification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "an end to end compression framework based on convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning for video compression")
  Member "sphereface deep hypersphere embedding for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "soft to hard vector quantization for end to end learning compressible representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to structure an image with few colors")
  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")
  Member "joint face detection and alignment using multitask cascaded convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")

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)