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: "pose guided human parsing with deep learned features"
  Abstract: Parsing human body into semantic regions is crucial to human-centric analysis. In this paper, we propose a segment-based parsing pipeline that explores human pose information, i.e. the joint location of a human model, which improves the part proposal, accelerates the inference and regularizes the parsing process at the same time. Specifically, we first generate part segment proposals with respect to human joints predicted by a deep model, then part- specific ranking models are trained for segment selection using both pose-based features and deep-learned part potential features. Finally, the best ensemble of the proposed part segments are inferred though an And-Or Graph. #R##N#We evaluate our approach on the popular Penn-Fudan pedestrian parsing dataset, and demonstrate the effectiveness of using the pose information for each stage of the parsing pipeline. Finally, we show that our approach yields superior part segmentation accuracy comparing to the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "zoom better to see clearer human part segmentation with auto zoom net"):
  with members:
    - "semantic image segmentation via deep parsing network"
    - "proposal free network for instance level object segmentation"
    - "joint object and part segmentation using deep learned potentials"
    - "attention to scale scale aware semantic image segmentation"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"
    - "deep learning for semantic part segmentation with high level guidance"
    - "densebox unifying landmark localization with end to end object detection"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "zoom better to see clearer human and object parsing with hierarchical auto zoom net"):
  with members:
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "you only look once unified real time object detection"
    - "proposal free network for instance level object segmentation"
    - "joint object and part segmentation using deep learned potentials"
    - "attention to scale scale aware semantic image segmentation"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"
    - "deep learning for semantic part segmentation with high level guidance"

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)