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 "deep frequent spatial temporal learning for face anti spoofing"):
  Abstract: Face anti-spoofing is crucial for the security of face recognition system, by avoiding invaded with presentation attack. Previous works have shown the effectiveness of using depth and temporal supervision for this task. However, depth supervision is often considered only in a single frame, and temporal supervision is explored by utilizing certain signals which is not robust to the change of scenes. In this work, motivated by two stream ConvNets, we propose a novel two stream FreqSaptialTemporalNet for face anti-spoofing which simultaneously takes advantage of frequent, spatial and temporal information. Compared with existing methods which mine spoofing cues in multi-frame RGB image, we make multi-frame spectrum image as one input stream for the discriminative deep neural network, encouraging the primary difference between live and fake video to be automatically unearthed. Extensive experiments show promising improvement results using the proposed architecture. Meanwhile, we proposed a concise method to obtain a large amount of spoofing training data by utilizing a frequent augmentation pipeline, which contributes detail visualization between live and fake images as well as data insufficiency issue when training large networks.

Includes members:
  Member "specular and diffuse reflection based face liveness detection for mobile devices":
    also connected through other hyperedges:
      (none)
  Member "learning generalizable and identity discriminative representations for face anti spoofing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "face attribute invertion")
  Member "feathernets convolutional neural networks as light as feather for face anti spoofing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "static and dynamic fusion for multi modal cross ethnicity face anti spoofing")
  Member "face de spoofing anti spoofing via noise modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "casia surf cefa a benchmark for multi modal cross ethnicity face anti spoofing")
  Member "discriminative representation combinations for accurate face spoofing detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep spatial gradient and temporal depth learning for face anti spoofing")
  Member "exploiting temporal and depth information for multi frame face anti spoofing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "searching central difference convolutional networks for face anti spoofing")
  Member "a dataset and benchmark for large scale multi modal face anti spoofing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "casia surf cefa a benchmark for multi modal cross ethnicity face anti spoofing")
  Member "joint 3d face reconstruction and dense alignment with position map regression network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "searching central difference convolutional networks for face anti spoofing")

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)