human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "actions speak louder than pass words passive authentication of smartphone users via deep temporal features"):
  Abstract: Prevailing user authentication schemes on smartphones rely on explicit user interaction, where a user types in a passcode or presents a biometric cue such as face, fingerprint, or iris. In addition to being cumbersome and obtrusive to the users, such authentication mechanisms pose security and privacy concerns. Passive authentication systems can tackle these challenges by frequently and unobtrusively monitoring the user's interaction with the device. In this paper, we propose a Siamese Long Short-Term Memory network architecture for passive authentication, where users can be verified without requiring any explicit authentication step. We acquired a dataset comprising of measurements from 30 smartphone sensor modalities for 37 users. We evaluate our approach on 8 dominant modalities, namely, keystroke dynamics, GPS location, accelerometer, gyroscope, magnetometer, linear accelerometer, gravity, and rotation sensors. Experimental results find that, within 3 seconds, a genuine user can be correctly verified 97.15% of the time at a false accept rate of 0.1%.

Includes members:
  Member "multilock mobile active authentication based on multiple biometric and behavioral patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "be captcha detecting human behavior in smartphone interaction using multiple inbuilt sensors")
  Member "active authentication on mobile devices via stylometry application usage web browsing and gps location":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "continuous authentication of smartphones based on application usage")
  Member "active user authentication for smartphones a challenge data set and benchmark results":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the resilience of biometric authentication systems against random inputs")
  Member "learning human identity from motion patterns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fedloc federated learning framework for cooperative localization and location data processing")
  Member "hmog new behavioral biometric features for continuous authentication of smartphone users":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a report on personally identifiable sensor data from smartphone devices")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "an hmm based behavior modeling approach for continuous mobile authentication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "kid on the phone toward automatic detection of children on mobile devices")
  Member "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the resilience of biometric authentication systems against random inputs")

Question: Please tell me which class the source paper 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.CV(Computer Vision and Pattern Recognition)