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: "hmog new behavioral biometric features for continuous authentication of smartphone users"
  Abstract: We introduce Hand Movement, Orientation, and Grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data was collected under two conditions: sitting and walking. We achieved authentication EERs as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at 16Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions, and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).

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"
    - "data driven authentication on the effectiveness of user behaviour modelling with mobile device sensors"
    - "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"
    - "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 "an active smartphone authentication method based on daily cyclical activity"):
  with members:
    - "deep convolutional and recurrent models for human activity recognition using wearables"

Connected through hyperedge HE-4 (induced by source paper "a comprehensive overview of biometric fusion"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "unravelling robustness of deep learning based face recognition against adversarial attacks"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "class representative autoencoder for low resolution multi spectral gender classification"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-5 (induced by source paper "a convolutional neural network for user identification based on motion sensors"):
  with members:
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "attention is all you need"
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "learning human identity from motion patterns"

Connected through hyperedge HE-6 (induced by source paper "authentication and authorization for mobile iot devices using bio features recent advances and future trends"):
  with members:
    - "blockchain technologies for the internet of things research issues and challenges"
    - "multimodal biometric authentication using choquet integral and genetic algorithm"
    - "a survey on behavioral biometric authentication on smartphones"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "partial face detection for continuous authentication"
    - "touchin sightless two factor authentication on multi touch mobile devices"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"
    - "a survey of biometric keystroke dynamics approaches security and challenges"

Connected through hyperedge HE-7 (induced by source paper "actions speak louder than pass words passive authentication of smartphone users via deep temporal features"):
  with members:
    - "multilock mobile active authentication based on multiple biometric and behavioral patterns"
    - "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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "continuous user authentication by contactless wireless sensing"):
  with members:
    - "large scale gan training for high fidelity natural image synthesis"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "adversarial examples in the physical world"
    - "freesense indoor human identification with wifi signals"
    - "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)