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: "learning human optical flow"
  Abstract: The optical flow of humans is well known to be useful for the analysis of human action. Given this, we devise an optical flow algorithm specifically for human motion and show that it is superior to generic flow methods. Designing a method by hand is impractical, so we develop a new training database of image sequences with ground truth optical flow. For this we use a 3D model of the human body and motion capture data to synthesize realistic flow fields. We then train a convolutional neural network to estimate human flow fields from pairs of images. Since many applications in human motion analysis depend on speed, and we anticipate mobile applications, we base our method on SpyNet with several modifications. We demonstrate that our trained network is more accurate than a wide range of top methods on held-out test data and that it generalizes well to real image sequences. When combined with a person detector/tracker, the approach provides a full solution to the problem of 2D human flow estimation. Both the code and the dataset are available for research.

Connected through hyperedge HE-1 (induced by source paper "learning multi human optical flow"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "learning from synthetic humans"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"

Connected through hyperedge HE-2 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue"):
  with members:
    - "does computer vision matter for action"
    - "learning the depths of moving people by watching frozen people"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "learning 3d human dynamics from video"

Connected through hyperedge HE-3 (induced by source paper "dynamonet dynamic action and motion network"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "self supervised learning of face representations for video face clustering"
    - "spatio temporal channel correlation networks for action classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "d3d distilled 3d networks for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-4 (induced by source paper "learning to dress 3d people in generative clothing"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "facsimile fast and accurate scans from an image in less than a second"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "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"
    - "learning to train with synthetic humans"

Connected through hyperedge HE-5 (induced by source paper "texturepose supervising human mesh estimation with texture consistency"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "expressive body capture 3d hands face and body from a single image"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning category specific mesh reconstruction from image collections"

Connected through hyperedge HE-6 (induced by source paper "learning to train with synthetic humans"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "image based synthesis for deep 3d human pose estimation"
    - "learning to refine human pose estimation"
    - "a closed form solution to photorealistic image stylization"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "how robust is 3d human pose estimation to occlusion"
    - "screenernet learning self paced curriculum for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "attacking optical flow"):
  with members:
    - "robust physical world attacks on deep learning models"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "the devil is in the decoder classification regression and gans"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-8 (induced by source paper "unsupervised generation of optical flow datasets"):
  with members:
    - "real world repetition estimation by div grad and curl"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "three for one and one for three flow segmentation and surface normals"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "accurate optical flow via direct cost volume processing"
    - "the 2017 davis challenge on video object segmentation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "im2flow motion hallucination from static images for action recognition"

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)