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: "raspireader open source fingerprint reader"
  Abstract: We open source an easy to assemble, spoof resistant, high resolution, optical fingerprint reader, called RaspiReader, using ubiquitous components. By using our open source STL files and software, RaspiReader can be built in under one hour for only US $175. As such, RaspiReader provides the fingerprint research community a seamless and simple method for quickly prototyping new ideas involving fingerprint reader hardware. In particular, we posit that this open source fingerprint reader will facilitate the exploration of novel fingerprint spoof detection techniques involving both hardware and software. We demonstrate one such spoof detection technique by specially customizing RaspiReader with two cameras for fingerprint image acquisition. One camera provides high contrast, frustrated total internal reflection (FTIR) fingerprint images, and the other outputs direct images of the finger in contact with the platen. Using both of these image streams, we extract complementary information which, when fused together and used for spoof detection, results in marked performance improvement over previous methods relying only on grayscale FTIR images provided by COTS optical readers. Finally, fingerprint matching experiments between images acquired from the FTIR output of RaspiReader and images acquired from a COTS reader verify the interoperability of the RaspiReader with existing COTS optical readers.

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 "universal material translator towards spoof fingerprint generalization"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fingerprint presentation attack detection generalization and efficiency"
    - "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"
    - "fingerprint presentation attack detection generalization and efficiency"
    - "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 "generalizing fingerprint spoof detector learning a one class classifier"):
  with members:
    - "fingerprint match in box"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "infant prints fingerprints for reducing infant mortality"):
  with members:
    - "fingerprints fixed length representation via deep networks and domain knowledge"
    - "fingerprint match in box"

Connected through hyperedge HE-6 (induced by source paper "fingerprint presentation attack detection generalization and efficiency"):
  with members:
    - "livdet 2017 fingerprint liveness detection competition 2017"
    - "fingerprint match in box"
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"
    - "universal 3d wearable fingerprint targets advancing fingerprint reader evaluations"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-7 (induced by source paper "fingerprint match in box"):
  with members:
    - "automated latent fingerprint recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)