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 "fingerprint spoof generalization"):
  Abstract: We present a style-transfer based wrapper, called Universal Material Generator (UMG), to improve the generalization performance of any fingerprint spoof detector against spoofs made from materials not seen during training. Specifically, we transfer the style (texture) characteristics between fingerprint images of known materials with the goal of synthesizing fingerprint images corresponding to unknown materials, that may occupy the space between the known materials in the deep feature space. Synthetic live fingerprint images are also added to the training dataset to force the CNN to learn generative-noise invariant features which discriminate between lives and spoofs. The proposed approach is shown to improve the generalization performance of a state-of-the-art spoof detector, namely Fingerprint Spoof Buster, from TDR of 75.24% to 91.78% @ FDR = 0.2%. These results are based on a large-scale dataset of 5,743 live and 4,912 spoof images fabricated using 12 different materials. Additionally, the UMG wrapper is shown to improve the average cross-sensor spoof detection performance from 67.60% to 80.63% when tested on the LivDet 2017 dataset. Training the UMG wrapper requires only 100 live fingerprint images from the target sensor, alleviating the time and resources required to generate large-scale live and spoof datasets for a new sensor. We also fabricate physical spoof artifacts using a mixture of known spoof materials to explore the role of cross-material style transfer in improving generalization performance.

Includes members:
  Member "biometric presentation attack detection beyond the visible spectrum":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cnn hyperparameter tuning applied to iris liveness detection")
  Member "universal material translator towards spoof fingerprint generalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "oct fingerprints resilience to presentation attacks")
  Member "generalizing fingerprint spoof detector learning a one class classifier":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fingerprint presentation attack detection based on local features encoding for unknown attacks")
  Member "oct fingerprints resilience to presentation attacks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a zero shot based fingerprint presentation attack detection system")
  Member "livdet in action fingerprint liveness detection competition 2019":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "oct fingerprints resilience to presentation attacks")
  Member "fingerprint presentation attack detection based on local features encoding for unknown attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fingerprint spoof detection temporal analysis of image sequence")
  Member "livdet 2017 fingerprint liveness detection competition 2017":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "biometric presentation attack detection beyond the visible spectrum")
  Member "fingerprint presentation attack detection generalization and efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "universal material translator towards spoof fingerprint generalization")

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)