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: "review of the fingerprint liveness detection livdet competition series 2009 to 2015"
  Abstract: Abstract   A spoof attack, a subset of presentation attacks, is the use of an artificial replica of a biometric in an attempt to circumvent a biometric sensor. Liveness detection, or presentation attack detection, distinguishes between live and fake biometric traits and is based on the principle that additional information can be garnered above and beyond the data procured by a standard authentication system to determine if a biometric measure is authentic.  The goals for the Liveness Detection (LivDet) competitions are to compare software-based fingerprint liveness detection and artifact detection algorithms (Part 1), as well as fingerprint systems which incorporate liveness detection or artifact detection capabilities (Part 2), using a standardized testing protocol and large quantities of spoof and live tests. The competitions are open to all academic and industrial institutions which have a solution for either software-based or system-based fingerprint liveness detection. The LivDet competitions have been hosted in 2009, 2011, 2013 and 2015 and have shown themselves to provide a crucial look at the current state of the art in liveness detection schemes. There has been a noticeable increase in the number of participants in LivDet competitions as well as a noticeable decrease in error rates across competitions. Participants have grown from four to the most recent thirteen submissions for Fingerprint Part 1. Fingerprints Part 2 has held steady at two submissions each competition in 2011 and 2013 and only one for the 2015 edition. The continuous increase of competitors demonstrates a growing interest in the topic.

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"
    - "livdet 2017 fingerprint liveness detection competition 2017"
    - "fair comparison of skin detection approaches on publicly available datasets"
    - "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 "a comprehensive overview of biometric fusion"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "unravelling robustness of deep learning based face recognition against adversarial attacks"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "class representative autoencoder for low resolution multi spectral gender classification"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-3 (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"
    - "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"
    - "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-4 (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"
    - "universal 3d wearable fingerprint targets advancing fingerprint reader evaluations"
    - "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-5 (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"
    - "raspireader open source fingerprint reader"
    - "cnn fixations an unraveling approach to visualize the discriminative image regions"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "analysis of user specific effect and impact of operator skills on fingerprint pad systems"):
  with members:
    - "livdet 2017 fingerprint liveness detection competition 2017"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "patch based fake fingerprint detection using a fully convolutional neural network with a small number of parameters and an optimal threshold"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "overfeat integrated recognition localization and detection using convolutional networks"

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)