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: "livdet 2017 fingerprint liveness detection competition 2017"
  Abstract: Fingerprint Presentation Attack Detection (FPAD) deals with distinguishing images coming from artificial replicas of the fingerprint characteristic, made up of materials like silicone, gelatine or latex, and images coming from alive fingerprints. Images are captured by modern scanners, typically relying on solid-state or optical technologies. Since from 2009, the Fingerprint Liveness Detection Competition (LivDet) aims to assess the performance of the state-of-the-art algorithms according to a rigorous experimental protocol and, at the same time, a simple overview of the basic achievements. The competition is open to all academics research centers and all companies that work in this field. The positive, increasing trend of the participants number, which supports the success of this initiative, is confirmed even this year: 17 algorithms were submitted to the competition, with a larger involvement of companies and academies. This means that the topic is relevant for both sides, and points out that a lot of work must be done in terms of fundamental and applied research.

Connected through hyperedge HE-1 (induced by source paper "biometric presentation attack detection beyond the visible spectrum"):
  with members:
    - "patch based fake fingerprint detection using a fully convolutional neural network with a small number of parameters and an optimal threshold"
    - "fair comparison of skin detection approaches on publicly available datasets"
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "planet photo geolocation with convolutional neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "defraudnet end2end fingerprint spoof detection using patch level attention"):
  with members:
    - "deep pixel wise binary supervision for face presentation attack detection"
    - "cbam convolutional block attention module"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "residual attention network for image classification"
    - "fingerprint spoof buster"
    - "densely connected convolutional networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-3 (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"
    - "fingerprint presentation attack detection generalization and efficiency"
    - "end to end latent fingerprint search"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "analysis of user specific effect and impact of operator skills on fingerprint pad systems"):
  with members:
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"

Connected through hyperedge HE-6 (induced by source paper "livdet in action fingerprint liveness detection competition 2019"):
  with members:
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-7 (induced by source paper "fingerprint presentation attack detection generalization and efficiency"):
  with members:
    - "fingerprint match in box"
    - "raspireader open source fingerprint reader"
    - "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"

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)