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: "fingerprint presentation attack detection generalization and efficiency"
  Abstract: We study the problem of fingerprint presentation attack detection (PAD) under unknown PA materials not seen during PAD training. A dataset of 5,743 bonafide and 4,912 PA images of 12 different materials is used to evaluate a state-of-the-art PAD, namely Fingerprint Spoof Buster. We utilize 3D t-SNE visualization and clustering of material characteristics to identify a representative set of PA materials that cover most of PA feature space. We observe that a set of six PA materials, namely Silicone, 2D Paper, Play Doh, Gelatin, Latex Body Paint and Monster Liquid Latex provide a good representative set that should be included in training to achieve generalization of PAD. We also propose an optimized Android app of Fingerprint Spoof Buster that can run on a commodity smartphone (Xiaomi Redmi Note 4) without a significant drop in PAD performance (from TDR = 95.7% to 95.3% @ FDR = 0.2%) which can make a PA prediction in less than 300ms.

Connected through hyperedge HE-1 (induced by source paper "fingerprint spoof generalization"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "universal material translator towards spoof fingerprint generalization"
    - "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"
    - "end to end latent fingerprint search"

Connected through hyperedge HE-2 (induced by source paper "universal material translator towards spoof fingerprint generalization"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "raspireader open source fingerprint reader"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "demystifying neural style transfer"
    - "texturegan controlling deep image synthesis with texture patches"
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-3 (induced by source paper "oct fingerprints resilience to presentation attacks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "universal material translator towards spoof fingerprint generalization"
    - "livdet in action fingerprint liveness detection competition 2019"
    - "livdet 2017 fingerprint liveness detection competition 2017"
    - "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-4 (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"
    - "universal material translator towards spoof fingerprint generalization"
    - "generalizing fingerprint spoof detector learning a one class classifier"
    - "livdet in action fingerprint liveness detection competition 2019"

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)