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 3d wearable fingerprint targets advancing fingerprint reader evaluations"
  Abstract: We present the design and manufacturing of high fidelity universal 3D fingerprint targets, which can be imaged on a variety of fingerprint sensing technologies, namely capacitive, contact-optical, and contactless-optical. Universal 3D fingerprint targets enable, for the first time, not only a repeatable and controlled evaluation of fingerprint readers, but also the ability to conduct fingerprint reader interoperability studies. Fingerprint reader interoperability refers to how robust fingerprint recognition systems are to variations in the images acquired by different types of fingerprint readers. To build universal 3D fingerprint targets, we adopt a molding and casting framework consisting of (i) digital mapping of fingerprint images to a negative mold, (ii) CAD modeling a scaffolding system to hold the negative mold, (iii) fabricating the mold and scaffolding system with a high resolution 3D printer, (iv) producing or mixing a material with similar electrical, optical, and mechanical properties to that of the human finger, and (v) fabricating a 3D fingerprint target using controlled casting. Our experiments conducted with PIV and Appendix F certified optical (contact and contactless) and capacitive fingerprint readers demonstrate the usefulness of universal 3D fingerprint targets for controlled and repeatable fingerprint reader evaluations and also fingerprint reader interoperability studies.

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"
    - "fingerprint presentation attack detection generalization and efficiency"

Connected through hyperedge HE-2 (induced by source paper "fingerprint spoof detection temporal analysis of image sequence"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "oct fingerprints resilience to presentation attacks"
    - "fingerprint presentation attack detection based on local features encoding for unknown attacks"
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "white box evaluation of fingerprint matchers robustness to minutiae perturbations"):
  with members:
    - "fingerprint distortion rectification using deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "fingerprint presentation attack detection generalization and efficiency"):
  with members:
    - "livdet 2017 fingerprint liveness detection competition 2017"
    - "fingerprint match in box"
    - "raspireader open source fingerprint reader"
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-5 (induced by source paper "raspireader open source fingerprint reader"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep representations for iris face and fingerprint spoofing detection"

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)