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: "evaluation of pose tracking accuracy in the first and second generations of microsoft kinect"
  Abstract: Microsoft Kinect camera and its skeletal tracking capabilities have been embraced by many researchers and commercial developers in various applications of real-time human movement analysis. In this paper, we evaluate the accuracy of the human kinematic motion data in the first and second generation of the Kinect system, and compare the results with an optical motion capture system. We collected motion data in 12 exercises for 10 different subjects and from three different viewpoints. We report on the accuracy of the joint localization and bone length estimation of Kinect skeletons in comparison to the motion capture. We also analyze the distribution of the joint localization offsets by fitting a mixture of Gaussian and uniform distribution models to determine the outliers in the Kinect motion data. Our analysis shows that overall Kinect 2 has more robust and more accurate tracking of human pose as compared to Kinect 1.

Connected through hyperedge HE-1 (induced by source paper "cardiopulmonary resuscitation quality parameters from motion capture data using differential evolution fitting of sinusoids"):
  with members:
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-2 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function"):
  with members:
    - "supervised learning of sparse context reconstruction coefficients for data representation and classification"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "representing data by sparse combination of contextual data points for classification"
    - "remote health coaching system and human motion data analysis for physical therapy with microsoft kinect"
    - "image tag completion by local learning"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "unsupervised temporal segmentation of repetitive human actions based on kinematic modeling and frequency analysis"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"

Connected through hyperedge HE-3 (induced by source paper "learning social affordance for human robot interaction"):
  with members:
    - "recurrent network models for human dynamics"
    - "joint inference of groups events and human roles in aerial videos"

Connected through hyperedge HE-4 (induced by source paper "remote health coaching system and human motion data analysis for physical therapy with microsoft kinect"):
  with members:
    - "unsupervised temporal segmentation of repetitive human actions based on kinematic modeling and frequency analysis"

Connected through hyperedge HE-5 (induced by source paper "unsupervised temporal segmentation of repetitive human actions based on kinematic modeling and frequency analysis"):
  with members:
    - "unstructured human activity detection from rgbd images"

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)