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 3d human dynamics from video"
  Abstract: From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future. This is because we have a mental model of 3D human dynamics that we have acquired from observing visual sequences of humans in motion. We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features. At test time, from video, the learned temporal representation give rise to smooth 3D mesh predictions. From a single image, our model can recover the current 3D mesh as well as its 3D past and future motion. Our approach is designed so it can learn from videos with 2D pose annotations in a semi-supervised manner. Though annotated data is always limited, there are millions of videos uploaded daily on the Internet. In this work, we harvest this Internet-scale source of unlabeled data by training our model on unlabeled video with pseudo-ground truth 2D pose obtained from an off-the-shelf 2D pose detector. Our experiments show that adding more videos with pseudo-ground truth 2D pose monotonically improves 3D prediction performance. We evaluate our model, Human Mesh and Motion Recovery (HMMR), on the recent challenging dataset of 3D Poses in the Wild and obtain state-of-the-art performance on the 3D prediction task without any fine-tuning. The project website with video, code, and data can be found at https://akanazawa.github.io/human_dynamics/.

Connected through hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation"):
  with members:
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "cross view fusion for 3d human pose estimation"
    - "learnable triangulation of human pose"
    - "distill knowledge from nrsfm for weakly supervised 3d pose learning"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "billion scale semi supervised learning for image classification"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "texturepose supervising human mesh estimation with texture consistency"

Connected through hyperedge HE-2 (induced by source paper "learning nonparametric human mesh reconstruction from a single image without ground truth meshes"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d scene reconstruction with multi layer depth and epipolar transformers"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "cross domain complementary learning with synthetic data for multi person part segmentation"
    - "end to end hand mesh recovery from a monocular rgb image"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "delving deep into hybrid annotations for 3d human recovery in the wild"
    - "detailed human shape estimation from a single image by hierarchical mesh deformation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "transferring dense pose to proximal animal classes"):
  with members:
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "deep learning tools for the measurement of animal behavior in neuroscience"
    - "billion scale semi supervised learning for image classification"
    - "unsupervised part based disentangling of object shape and appearance"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "unsupervised learning of landmarks by descriptor vector exchange"

Connected through hyperedge HE-5 (induced by source paper "anatomy aware 3d human pose estimation in videos"):
  with members:
    - "chirality nets for human pose regression"
    - "trajectory space factorization for deep video based 3d human pose estimation"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "generating multiple hypotheses for 3d human pose estimation with mixture density network"
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "2d 3d pose estimation and action recognition using multitask deep learning"

Connected through hyperedge HE-6 (induced by source paper "back to the future joint aware temporal deep learning 3d human pose estimation"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "deep high resolution representation learning for human pose estimation"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "modeling human motion with quaternion based neural networks"
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "2d 3d pose estimation and action recognition using multitask deep learning"

Connected through hyperedge HE-7 (induced by source paper "post estimation smoothing a simple baseline for learning with side information"):
  with members:
    - "predicting 3d human dynamics from video"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "end to end recovery of human shape and pose"
    - "learning 3d human pose from structure and motion"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-8 (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 human optical flow"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised learning of object landmarks through conditional image generation"

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)