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: "data driven authentication on the effectiveness of user behaviour modelling with mobile device sensors"
  Abstract: We propose a lightweight, and temporally and spatially aware user behaviour modelling technique for sensor-based authentication. Operating in the background, our data driven technique compares current behaviour with a user profile. If the behaviour deviates sufficiently from the established norm, actions such as explicit authentication can be triggered. To support a quick and lightweight deployment, our solution automatically switches from training mode to deployment mode when the user's behaviour is sufficiently learned. Furthermore, it allows the device to automatically determine a suitable detection threshold. We use our model to investigate practical aspects of sensor-based authentication by applying it to three publicly available data sets, computing expected times for training duration and behaviour drift. We also test our model with scenarios involving an attacker with varying knowledge and capabilities.

Connected through hyperedge HE-1 (induced by source paper "a report on personally identifiable sensor data from smartphone devices"):
  with members:
    - "predicting floor level for 911 calls with neural networks and smartphone sensor data"
    - "mobile phone identification through the built in magnetometers"
    - "planet photo geolocation with convolutional neural networks"
    - "seeing behind the camera identifying the authorship of a photograph"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "pin skimming exploiting the ambient light sensor in mobile devices"
    - "mobile device identification via sensor fingerprinting"
    - "an egocentric look at video photographer identity"

Connected through hyperedge HE-2 (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"
    - "multi sensor authentication to improve smartphone security"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "secure voice based authentication for mobile devices vaulted voice verification"

Connected through hyperedge HE-3 (induced by source paper "continuous implicit authentication for mobile devices based on adaptive neuro fuzzy inference system"):
  with members:
    - "active authentication on mobile devices via stylometry application usage web browsing and gps location"
    - "fuzzy logic based implicit authentication for mobile access control"
    - "event driven implicit authentication for mobile access control"
    - "conxsense context sensing for adaptive usable access control"
    - "implementation and evaluation of measurement based admission control schemes within a converged networks qos management framework"

Connected through hyperedge HE-4 (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"
    - "multi sensor authentication to improve smartphone security"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

Connected through hyperedge HE-5 (induced by source paper "a survey on behavioral biometric authentication on smartphones"):
  with members:
    - "learning human identity from motion patterns"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

Connected through hyperedge HE-6 (induced by source paper "changing users security behaviour towards security questions a game based learning approach"):
  with members:
    - "security strength indicator in fallback authentication nudging users for better answers in secret questions"
    - "naturally rehearsing passwords"

Connected through hyperedge HE-7 (induced by source paper "a gamified approach to improve users memorability of fall back authentication"):
  with members:
    - "security strength indicator in fallback authentication nudging users for better answers in secret questions"
    - "naturally rehearsing passwords"

Connected through hyperedge HE-8 (induced by source paper "implicit sensor based authentication of smartphone users with smartwatch"):
  with members:
    - "multi sensor authentication to improve smartphone security"

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)