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: "3d semantic trajectory reconstruction from 3d pixel continuum"
  Abstract: This paper presents a method to reconstruct dense semantic trajectory stream of human interactions in 3D from synchronized multiple videos. The interactions inherently introduce self-occlusion and illumination/appearance/shape changes, resulting in highly fragmented trajectory reconstruction with noisy and coarse semantic labels. Our conjecture is that among many views, there exists a set of views that can confidently recognize the visual semantic label of a 3D trajectory. We introduce a new representation called 3D semantic map---a probability distribution over the semantic labels per trajectory. We construct the 3D semantic map by reasoning about visibility and 2D recognition confidence based on view-pooling, i.e., finding the view that best represents the semantics of the trajectory. Using the 3D semantic map, we precisely infer all trajectory labels jointly by considering the affinity between long range trajectories via estimating their local rigid transformations. This inference quantitatively outperforms the baseline approaches in terms of predictive validity, representation robustness, and affinity effectiveness. We demonstrate that our algorithm can robustly compute the semantic labels of a large scale trajectory set involving real-world human interactions with object, scenes, and people.

Connected through hyperedge HE-1 (induced by source paper "monet multiview semi supervised keypoint detection via epipolar divergence"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "densepose dense human pose estimation in the wild"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-2 (induced by source paper "humbi a large multiview dataset of human body expressions"):
  with members:
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "self supervised adaptation of high fidelity face models for monocular performance tracking"
    - "learning joint reconstruction of hands and manipulated objects"
    - "towards social artificial intelligence nonverbal social signal prediction in a triadic interaction"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "deep appearance models for face rendering"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning to estimate 3d hand pose from single rgb images"

Connected through hyperedge HE-3 (induced by source paper "multiview supervision by registration"):
  with members:
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "end to end recovery of human shape and pose"
    - "unsupervised learning of depth and ego motion from video"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "sfm net learning of structure and motion from video"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "humbi 1 0 human multiview behavioral imaging dataset"):
  with members:
    - "deep appearance models for face rendering"
    - "hand keypoint detection in single images using multiview bootstrapping"

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)