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: "multilock mobile active authentication based on multiple biometric and behavioral patterns"
  Abstract: In this paper we evaluate mobile active authentication based on an ensemble of biometrics and behavior-based profiling signals. We consider seven different data channels and their combination. Touch dynamics (touch gestures and keystroking), accelerometer, gyroscope, WiFi, GPS location and app usage are all collected during human-mobile interaction to authenticate the users. We evaluate two approaches: one-time authentication and active authentication. In one-time authentication, we employ the information of all channels available during one session. For active authentication we take advantage of mobile user behavior across multiple sessions by updating a confidence value of the authentication score. Our experiments are conducted on the semi-uncontrolled UMDAA-02 database. This database comprises smartphone sensor signals acquired during natural human-mobile interaction. Our results show that different traits can be complementary and multimodal systems clearly increase the performance with accuracies ranging from 82.2% to 97.1% depending on the authentication scenario.

Connected through hyperedge HE-1 (induced by source paper "be captcha detecting human behavior in smartphone interaction using multiple inbuilt sensors"):
  with members:
    - "actions speak louder than pass words passive authentication of smartphone users via deep temporal features"
    - "hacking google recaptcha v3 using reinforcement learning"
    - "mobiface a novel dataset for mobile face tracking in the wild"
    - "continuous authentication of smartphones based on application usage"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "path person authentication using trace histories"
    - "the partially observable hidden markov model and its application to keystroke dynamics"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

Connected through hyperedge HE-2 (induced by source paper "sensor based continuous authentication of smartphones users using behavioral biometrics a survey"):
  with members:
    - "authentication of smartphone users using behavioral biometrics"
    - "implicit smartphone user authentication with sensors and contextual machine learning"
    - "multi sensor authentication to improve smartphone security"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "data driven authentication on the effectiveness of user behaviour modelling with mobile device sensors"
    - "secure voice based authentication for mobile devices vaulted voice verification"

Connected through hyperedge HE-3 (induced by source paper "actions speak louder than pass words passive authentication of smartphone users via deep temporal features"):
  with members:
    - "active authentication on mobile devices via stylometry application usage web browsing and gps location"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "learning human identity from motion patterns"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "adam a method for stochastic optimization"
    - "an hmm based behavior modeling approach for continuous mobile authentication"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

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)