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: "temporally coherent 4d reconstruction of complex dynamic scenes"
  Abstract: This paper presents an approach for reconstruction of 4D temporally coherent models of complex dynamic scenes. No prior knowledge is required of scene structure or camera calibration allowing reconstruction from multiple moving cameras. Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects. Temporal coherence is exploited to overcome visual ambiguities resulting in improved reconstruction of complex scenes. Robust joint segmentation and reconstruction of dynamic objects is achieved by introducing a geodesic star convexity constraint. Comparative evaluation is performed on a variety of unstructured indoor and outdoor dynamic scenes with hand-held cameras and multiple people. This demonstrates reconstruction of complete temporally coherent 4D scene models with improved nonrigid object segmentation and shape reconstruction.

Connected through hyperedge HE-1 (induced by source paper "u4d unsupervised 4d dynamic scene understanding"):
  with members:
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "4d temporally coherent light field video"
    - "continuous 3d label stereo matching using local expansion moves"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "accurate optical flow via direct cost volume processing"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "towards accurate markerless human shape and pose estimation over time"
    - "joint multi person pose estimation and semantic part segmentation"

Connected through hyperedge HE-2 (induced by source paper "running event visualization using videos from multiple cameras"):
  with members:
    - "bag of tricks and a strong baseline for deep person re identification"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "scene text detection and recognition the deep learning era"
    - "survey on deep learning techniques for person re identification task"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "in defense of the triplet loss for person re identification"
    - "east an efficient and accurate scene text detector"

Connected through hyperedge HE-3 (induced by source paper "temporally coherent general dynamic scene reconstruction"):
  with members:
    - "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
    - "learning 3d shape completion under weak supervision"
    - "specular to diffuse translation for multi view reconstruction"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "coherent motion segmentation in moving camera videos using optical flow orientations"

Connected through hyperedge HE-4 (induced by source paper "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"):
  with members:
    - "learning word like units from joint audio visual analysis"
    - "english conversational telephone speech recognition by humans and machines"
    - "the zero resource speech challenge 2017"
    - "are words easier to learn from infant than adult directed speech a quantitative corpus based investigation"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "deep neural networks predict hierarchical spatio temporal cortical dynamics of human visual object recognition"
    - "building machines that learn and think like people"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-5 (induced by source paper "reticam real time human performance capture from monocular video"):
  with members:
    - "bodynet volumetric inference of 3d human body shapes"
    - "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"
    - "densepose dense human pose estimation in the wild"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-6 (induced by source paper "robust 3d human motion reconstruction via dynamic template construction"):
  with members:
    - "model based outdoor performance capture"
    - "capturing dynamic textured surfaces of moving targets"
    - "general automatic human shape and motion capture using volumetric contour cues"
    - "general dynamic scene reconstruction from multiple view video"

Connected through hyperedge HE-7 (induced by source paper "4d temporally coherent light field video"):
  with members:
    - "dense human body correspondences using convolutional networks"

Connected through hyperedge HE-8 (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"
    - "general automatic human shape and motion capture using volumetric contour cues"
    - "fast and robust hand tracking using detection guided optimization"
    - "general dynamic scene reconstruction from multiple view video"
    - "efficient multi view performance capture of fine scale surface detail"

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)