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: "implicit smartphone user authentication with sensors and contextual machine learning"
  Abstract: Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.

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"
    - "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-2 (induced by source paper "smartphone impostor detection with built in sensors and deep learning"):
  with members:
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-3 (induced by source paper "detecting deep neural network defects with data flow analysis"):
  with members:
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "testing deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-4 (induced by source paper "effect of context in swipe gesture based continuous authentication on smartphones"):
  with members:
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"

Connected through hyperedge HE-5 (induced by source paper "kid on the phone toward automatic detection of children on mobile devices"):
  with members:
    - "tap based user authentication for smartwatches"
    - "illusionpin shoulder surfing resistant authentication using hybrid images"
    - "an hmm based multi sensor approach for continuous mobile authentication"
    - "an hmm based behavior modeling approach for continuous mobile authentication"
    - "user generated free form gestures for authentication security and memorability"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"
    - "silentsense silent user identification via dynamics of touch and movement behavioral biometrics"

Connected through hyperedge HE-6 (induced by source paper "secure pick up implicit authentication when you start using the smartphone"):
  with members:
    - "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"

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)