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 a new biometric modality for continuous authentication of smartphone users"
  Abstract: In this paper, we introduce Hand Movement, Orientation, and Grasp (HMOG), a behavioral biometric to continuously authenticate smartphone users. HMOG unobtrusively captures subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated the authentication and biometric key generation (BKG) performance of HMOG features on typing data collected from 100 subjects. Data was collected under two conditions: sitting and walking. We achieved authentication EERs as low as 6.92% (walking) and 10.20% (sitting) when we combined HMOG, tap, and keystroke features. We performed additional 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 13.7% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features led to EERs between 31.3% and 36.7%. 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.

Connected through hyperedge HE-1 (induced by source paper "learning human identity from motion patterns"):
  with members:
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "exploring ways to mitigate sensor based smartphone fingerprinting"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "deep speech scaling up end to end speech recognition"
    - "a clockwork rnn"
    - "moddrop adaptive multi modal gesture recognition"
    - "using unlabeled data in a sparse coding framework for human activity recognition"

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)