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: "assessment of gait normality using a depth camera and mirrors"
  Abstract: This paper presents an initial work on assessment of gait normality in which the human body motion is repre­sented by a sequence of enhanced depth maps. The input data is provided by a system consisting of a Time-of-Flight (ToF) depth camera and two mirrors. This approach proposes two feature types to describe characteristics of localized points of interest and the level of posture symmetry. These two features are processed on a sequence of enhanced depth maps with the support of a sliding window to provide two corresponding scores. The gait assessment is finally performed based on a weighted combination of these two scores. The evaluation is performed by experimenting on 6 simulated abnormal gaits.

Connected through hyperedge HE-1 (induced by source paper "applying adversarial auto encoder for estimating human walking gait abnormality index"):
  with members:
    - "matching based depth camera and mirrors for 3d reconstruction"
    - "stabilizing training of generative adversarial networks through regularization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "skeleton based gait index estimation with lstms"):
  with members:
    - "unsupervised learning of video representations using lstms"
    - "recurrent neural network regularization"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "human gait symmetry assessment using a depth camera and mirrors"):
  with members:
    - "matching based depth camera and mirrors for 3d reconstruction"

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.CV(Computer Vision and Pattern Recognition)