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: "universal material translator towards spoof fingerprint generalization"
  Abstract: Spoof detectors are classifiers that are trained to distinguish spoof fingerprints from bonafide ones. However, state of the art spoof detectors do not generalize well on unseen spoof materials. This study proposes a style transfer based augmentation wrapper that can be used on any existing spoof detector and can dynamically improve the robustness of the spoof detection system on spoof materials for which we have very low data. Our method is an approach for synthesizing new spoof images from a few spoof examples that transfers the style or material properties of the spoof examples to the content of bonafide fingerprints to generate a larger number of examples to train the classifier on. We demonstrate the effectiveness of our approach on materials in the publicly available LivDet 2015 dataset and show that the proposed approach leads to robustness to fingerprint spoofs of the target material.

Connected through hyperedge HE-1 (induced by source paper "fingerprint spoof generalization"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "generalizing fingerprint spoof detector learning a one class classifier"
    - "oct fingerprints resilience to presentation attacks"
    - "livdet in action fingerprint liveness detection competition 2019"
    - "fingerprint presentation attack detection based on local features encoding for unknown attacks"
    - "livdet 2017 fingerprint liveness detection competition 2017"
    - "fingerprint presentation attack detection generalization and efficiency"
    - "end to end latent fingerprint search"

Connected through hyperedge HE-2 (induced by source paper "oct fingerprints resilience to presentation attacks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "livdet in action fingerprint liveness detection competition 2019"
    - "livdet 2017 fingerprint liveness detection competition 2017"
    - "fingerprint presentation attack detection generalization and efficiency"
    - "raspireader open source fingerprint reader"
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"
    - "cnn fixations an unraveling approach to visualize the discriminative image regions"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "fingerprint presentation attack detection based on local features encoding for unknown attacks"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "generalizing fingerprint spoof detector learning a one class classifier"
    - "livdet in action fingerprint liveness detection competition 2019"
    - "fingerprint presentation attack detection generalization and efficiency"

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)