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: "an overview of face liveness detection"
  Abstract: Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.

Connected through hyperedge HE-1 (induced by source paper "feathernets convolutional neural networks as light as feather for face anti spoofing"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "arcface additive angular margin loss for deep face recognition"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"

Connected through hyperedge HE-2 (induced by source paper "a survey on modality characteristics performance evaluation metrics and security for traditional and wearable biometric systems"):
  with members:
    - "survey on synchrophasor data quality and cybersecurity challenges and evaluation of their interdependencies"
    - "secure management of low power fitness trackers"
    - "secure management of low power fitness trackers"
    - "handwritten signature verification using hand worn devices"
    - "evolution toward 5g multi tier cellular wireless networks an interference management perspective"
    - "enhancing the accuracy of biometric feature extraction fusion using gabor filter and mahalanobis distance algorithm"
    - "bad data injection attack and defense in electricity market using game theory study"
    - "fast computation of the performance evaluation of biometric systems application to multibiometrics"

Connected through hyperedge HE-3 (induced by source paper "face flashing a secure liveness detection protocol based on light reflections"):
  with members:
    - "paraph presentation attack rejection by analyzing polarization hypotheses"
    - "convolutional channel features"
    - "caffe convolutional architecture for fast feature embedding"

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)