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 "you are how you walk uncooperative mocap gait identification for video surveillance with incomplete and noisy data"):
  Abstract: This work offers a design of a video surveillance system based on a soft biometric -- gait identification from MoCap data. The main focus is on two substantial issues of the video surveillance scenario: (1) the walkers do not cooperate in providing learning data to establish their identities and (2) the data are often noisy or incomplete. We show that only a few examples of human gait cycles are required to learn a projection of raw MoCap data onto a low-dimensional sub-space where the identities are well separable. Latent features learned by Maximum Margin Criterion (MMC) method discriminate better than any collection of geometric features. The MMC method is also highly robust to noisy data and works properly even with only a fraction of joints tracked. The overall workflow of the design is directly applicable for a day-to-day operation based on the available MoCap technology and algorithms for gait analysis. In the concept we introduce, a walker's identity is represented by a cluster of gait data collected at their incidents within the surveillance system: They are how they walk.

Includes members:
  Member "gait recognition from motion capture data":
    also connected through other hyperedges:
      (none)
  Member "an evaluation framework and database for mocap based gait recognition methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gait recognition from motion capture data")
  Member "active user authentication for smartphones a challenge data set and benchmark results":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the resilience of biometric authentication systems against random inputs")
  Member "learning robust features for gait recognition by maximum margin criterion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gait recognition from motion capture data")
  Member "person re identification by unsupervised video matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "walker independent features for gait recognition from motion capture data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gait recognition from motion capture data")
  Member "path person authentication using trace histories":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "be captcha detecting human behavior in smartphone interaction using multiple inbuilt sensors")
  Member "person re identification past present and future":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")

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)