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: "handwritten signature verification using hand worn devices"
  Abstract: Online signature verification technologies, such as those available in banks and post offices, rely on dedicated digital devices such as tablets or smart pens to capture, analyze and verify signatures. In this paper, we suggest a novel method for online signature verification that relies on the increasingly available hand-worn devices, such as smartwatches or fitness trackers, instead of dedicated ad-hoc devices. Our method uses a set of known genuine and forged signatures, recorded using the motion sensors of a hand-worn device, to train a machine learning classifier. Then, given the recording of an unknown signature and a claimed identity, the classifier can determine whether the signature is genuine or forged. In order to validate our method, it was applied on 1980 recordings of genuine and forged signatures that we collected from 66 subjects in our institution. Using our method, we were able to successfully distinguish between genuine and forged signatures with a high degree of accuracy (0.98 AUC and 0.05 EER).

Connected through hyperedge HE-1 (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"
    - "evolution toward 5g multi tier cellular wireless networks an interference management perspective"
    - "an overview of face liveness detection"
    - "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-2 (induced by source paper "fmcode a 3d in the air finger motion based user login framework for gesture interface"):
  with members:
    - "fmhash deep hashing of in air handwriting for user identification"
    - "airdraw leveraging smart watch motion sensors for mobile human computer interactions"
    - "xception deep learning with depthwise separable convolutions"
    - "forgery resistant touch based authentication on mobile devices"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "engineering gesture based authentication systems"

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.CR(Cryptography and Security)