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 "searching central difference convolutional networks for face anti spoofing"):
  Abstract: Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most state-of-the-art FAS methods 1) rely on stacked convolutions and expert-designed network, which is weak in describing detailed fine-grained information and easily being ineffective when the environment varies (e.g., different illumination), and 2) prefer to use long sequence as input to extract dynamic features, making them difficult to deploy into scenarios which need quick response. Here we propose a novel frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information. A network built with CDC, called the Central Difference Convolutional Network (CDCN), is able to provide more robust modeling capacity than its counterpart built with vanilla convolution. Furthermore, over a specifically designed CDC search space, Neural Architecture Search (NAS) is utilized to discover a more powerful network structure (CDCN++), which can be assembled with Multiscale Attention Fusion Module (MAFM) for further boosting performance. Comprehensive experiments are performed on six benchmark datasets to show that 1) the proposed method not only achieves superior performance on intra-dataset testing (especially 0.2% ACER in Protocol-1 of OULU-NPU dataset), 2) it also generalizes well on cross-dataset testing (particularly 6.5% HTER from CASIA-MFSD to Replay-Attack datasets). The codes are available at \href{https://github.com/ZitongYu/CDCN}{https://github.com/ZitongYu/CDCN}.

Includes members:
  Member "video action recognition via neural architecture searching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rethinking motion representation residual frames with 3d convnets for better action recognition")
  Member "pc darts partial channel connections for memory efficient differentiable architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nas count counting by density with neural architecture search")
  Member "deep pixel wise binary supervision for face presentation attack detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "defraudnet end2end fingerprint spoof detection using patch level attention")
  Member "deep tree learning for zero shot face anti spoofing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "neural architecture search for deep face recognition":
    also connected through other hyperedges:
      (none)
  Member "searching for a robust neural architecture in four gpu hours":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning architectures for binary networks")
  Member "customizable architecture search for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast neural network adaptation via parameter remapping and architecture search")
  Member "auto reid searching for a part aware convnet for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a strong baseline for fashion retrieval with person re identification models")

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)