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: "multi sensor authentication to improve smartphone security"
  Abstract: The widespread use of smartphones gives rise to new security and privacy concerns. Smartphone thefts account for the largest percentage of thefts in recent crime statistics. Using a victim's smartphone, the attacker can launch impersonation attacks, which threaten the security of the victim and other users in the network. Our threat model includes the attacker taking over the phone after the user has logged on with his password or pin. Our goal is to design a mechanism for smartphones to better authenticate the current user, continuously and implicitly, and raise alerts when necessary. In this paper, we propose a multi-sensors-based system to achieve continuous and implicit authentication for smartphone users. The system continuously learns the owner's behavior patterns and environment characteristics, and then authenticates the current user without interrupting user-smartphone interactions. Our method can adaptively update a user's model considering the temporal change of user's patterns. Experimental results show that our method is efficient, requiring less than 10 seconds to train the model and 20 seconds to detect the abnormal user, while achieving high accuracy (more than 90%). Also the combination of more sensors provide better accuracy. Furthermore, our method enables adjusting the security level by changing the sampling rate.

Connected through hyperedge HE-1 (induced by source paper "sensor based continuous authentication of smartphones users using behavioral biometrics a survey"):
  with members:
    - "multilock mobile active authentication based on multiple biometric and behavioral patterns"
    - "authentication of smartphone users using behavioral biometrics"
    - "implicit smartphone user authentication with sensors and contextual machine learning"
    - "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-2 (induced by source paper "deeprotect enabling inference based access control on mobile sensing applications"):
  with members:
    - "6thsense a context aware sensor based attack detector for smart devices"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "caffe convolutional architecture for fast feature embedding"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"
    - "participatory privacy enabling privacy in participatory sensing"
    - "placeraider virtual theft in physical spaces with smartphones"
    - "blind compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "implicit smartphone user authentication with sensors and contextual machine learning"):
  with members:
    - "secure pick up implicit authentication when you start using the smartphone"
    - "implicit sensor based authentication of smartphone users with smartwatch"
    - "data driven authentication on the effectiveness of user behaviour modelling with mobile device sensors"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

Connected through hyperedge HE-4 (induced by source paper "secure pick up implicit authentication when you start using the smartphone"):
  with members:
    - "implicit smartphone user authentication with sensors and contextual machine learning"
    - "implicit sensor based authentication of smartphone users with smartwatch"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

Connected through hyperedge HE-5 (induced by source paper "implicit sensor based authentication of smartphone users with smartwatch"):
  with members:
    - "data driven authentication on the effectiveness of user behaviour modelling with mobile device sensors"

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)