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: "general dynamic scene reconstruction from multiple view video"
  Abstract: This paper introduces a general approach to dynamic scene reconstruction from multiple moving cameras without prior knowledge or limiting constraints on the scene structure, appearance, or illumination. Existing techniques for dynamic scene reconstruction from multiple wide-baseline camera views primarily focus on accurate reconstruction in controlled environments, where the cameras are fixed and calibrated and background is known. These approaches are not robust for general dynamic scenes captured with sparse moving cameras. Previous approaches for outdoor dynamic scene reconstruction assume prior knowledge of the static background appearance and structure. The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras. Evaluation is performed on a variety of indoor and outdoor scenes with cluttered backgrounds and multiple dynamic non-rigid objects such as people. Comparison with state-of-the-art approaches demonstrates improved accuracy in both multiple view segmentation and dense reconstruction. The proposed approach also eliminates the requirement for prior knowledge of scene structure and appearance.

Connected through hyperedge HE-1 (induced by source paper "deepcap monocular human performance capture using weak supervision"):
  with members:
    - "tailornet predicting clothing in 3d as a function of human pose shape and garment style"
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "tex2shape detailed full human body geometry from a single image"
    - "learning to dress 3d people in generative clothing"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"
    - "moulding humans non parametric 3d human shape estimation from single images"

Connected through hyperedge HE-2 (induced by source paper "learning dense wide baseline stereo matching for people"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "stereodrnet dilated residual stereo net"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning from synthetic data addressing domain shift for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "robust 3d human motion reconstruction via dynamic template construction"):
  with members:
    - "temporally coherent 4d reconstruction of complex dynamic scenes"
    - "model based outdoor performance capture"
    - "capturing dynamic textured surfaces of moving targets"
    - "general automatic human shape and motion capture using volumetric contour cues"

Connected through hyperedge HE-4 (induced by source paper "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"):
  with members:
    - "video based reconstruction of 3d people models"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"

Connected through hyperedge HE-5 (induced by source paper "joint 3d reconstruction of a static scene and moving objects"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-6 (induced by source paper "monoperfcap human performance capture from monocular video"):
  with members:
    - "towards accurate markerless human shape and pose estimation over time"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "structured prediction of 3d human pose with deep neural networks"
    - "model based outdoor performance capture"
    - "capturing dynamic textured surfaces of moving targets"

Connected through hyperedge HE-7 (induced by source paper "model based outdoor performance capture"):
  with members:
    - "building statistical shape spaces for 3d human modeling"
    - "a versatile scene model with differentiable visibility applied to generative pose estimation"
    - "temporally coherent 4d reconstruction of complex dynamic scenes"
    - "general automatic human shape and motion capture using volumetric contour cues"
    - "fast and robust hand tracking using detection guided optimization"
    - "efficient multi view performance capture of fine scale surface detail"

Connected through hyperedge HE-8 (induced by source paper "dense wide baseline scene flow from two handheld video cameras"):
  with members:
    - "real time halfway domain reconstruction of motion and geometry"
    - "wxbs wide baseline stereo generalizations"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

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)