human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "meta anti spoofing learning to learn in face anti spoofing"
  Abstract: Face anti-spoofing is crucial to the security of face recognition systems. Previously, most methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks (PA). However, new attack methods keep evolving that produce new forms of spoofing faces to compromise the existing detectors. This requires researchers to collect a large number of samples to train classifiers for detecting new attacks, which is often costly and leads the later newly evolved attack samples to remain in small scales. Alternatively, we define face anti-spoofing as a few-shot learning problem with evolving new attacks and propose a novel face anti-spoofing approach via meta-learning named Meta Face Anti-spoofing (Meta-FAS). Meta-FAS addresses the above-mentioned problems by training the classifiers how to learn to detect the spoofing faces with few examples. To assess the effectiveness of the proposed approach, we propose a series of evaluation benchmarks based on public datasets (\textit{e.g.}, OULU-NPU, SiW, CASIA-MFSD, Replay-Attack, MSU-MFSD, 3D-MAD, and CASIA-SURF), and the proposed approach shows its superior performances to compared methods.

Connected through hyperedge HE-1 (induced by source paper "style transfer applied to face liveness detection with user centered models"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "face de spoofing anti spoofing via noise modeling"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "how far did we get in face spoofing detection"
    - "a style based generator architecture for generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Question: Please tell me which class the center node 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)