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: "a latent clothing attribute approach for human pose estimation"
  Abstract: As a fundamental technique that concerns several vision tasks such as image parsing, action recognition and clothing retrieval, human pose estimation (HPE) has been extensively investigated in recent years. To achieve accurate and reliable estimation of the human pose, it is well-recognized that the clothing attributes are useful and should be utilized properly. Most previous approaches, however, require to manually annotate the clothing attributes and are therefore very costly. In this paper, we shall propose and explore a \emph{latent} clothing attribute approach for HPE. Unlike previous approaches, our approach models the clothing attributes as latent variables and thus requires no explicit labeling for the clothing attributes. The inference of the latent variables are accomplished by utilizing the framework of latent structured support vector machines (LSSVM). We employ the strategy of \emph{alternating direction} to train the LSSVM model: In each iteration, one kind of variables (e.g., human pose or clothing attribute) are fixed and the others are optimized. Our extensive experiments on two real-world benchmarks show the state-of-the-art performance of our proposed approach.

Connected through hyperedge HE-1 (induced by source paper "survey on emotional body gesture recognition"):
  with members:
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "3d human pose estimation 2d pose estimation matching"
    - "monocular 3d human pose estimation in the wild using improved cnn supervision"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "associative embedding end to end learning for joint detection and grouping"

Connected through hyperedge HE-2 (induced by source paper "deep view sensitive pedestrian attribute inference in an end to end model"):
  with members:
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "attribute recognition from adaptive parts"
    - "a richly annotated dataset for pedestrian attribute recognition"
    - "multi task curriculum transfer deep learning of clothing attributes"
    - "attribute and or grammar for joint parsing of human attributes part and pose"
    - "weakly supervised learning of mid level features for pedestrian attribute recognition and localization"
    - "contextual action recognition with r cnn"
    - "fast r cnn"

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)