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 pose grammar to encode human body configuration for 3d pose estimation"
  Abstract: In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation. Our model directly takes 2D pose as input and learns a generalized 2D-3D mapping function. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNN) on the top to explicitly incorporate a set of knowledge regarding human body configuration (i.e., kinematics, symmetry, motor coordination). The proposed model thus enforces high-level constraints over human poses. In learning, we develop a pose sample simulator to augment training samples in virtual camera views, which further improves our model generalizability. We validate our method on public 3D human pose benchmarks and propose a new evaluation protocol working on cross-view setting to verify the generalization capability of different methods. We empirically observe that most state-of-the-art methods encounter difficulty under such setting while our method can well handle such challenges.

Connected through hyperedge HE-1 (induced by source paper "hierarchical human parsing with typed part relation reasoning"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "zero shot video object segmentation via attentive graph neural networks"
    - "graspnet a large scale clustered and densely annotated datase for object grasping"
    - "reasoning visual dialogs with structural and partial observations"
    - "understanding human gaze communication by spatio temporal graph reasoning"
    - "graphonomy universal human parsing via graph transfer learning"
    - "spgnet semantic prediction guidance for scene parsing"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"

Connected through hyperedge HE-2 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation"):
  with members:
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "ordinal depth supervision for 3d human pose estimation"
    - "multi scale structure aware network for human pose estimation"
    - "yolov3 an incremental improvement"
    - "video based reconstruction of 3d people models"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "how robust is 3d human pose estimation to occlusion"
    - "3d human pose estimation in the wild by adversarial learning"

Connected through hyperedge HE-3 (induced by source paper "hemlets posh learning part centric heatmap triplets for 3d human pose and shape estimation"):
  with members:
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "denserac joint 3d pose and shape estimation by dense render and compare"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "hemlets pose learning part centric heatmap triplets for accurate 3d human pose estimation"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "densebody directly regressing dense 3d human pose and shape from a single color image"

Connected through hyperedge HE-4 (induced by source paper "hierarchical kinematic human mesh recovery"):
  with members:
    - "repose learning deep kinematic priors for fast human pose estimation"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "denserac joint 3d pose and shape estimation by dense render and compare"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "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"
    - "densebody directly regressing dense 3d human pose and shape from a single color image"

Connected through hyperedge HE-5 (induced by source paper "learning compositional neural information fusion for human parsing"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "instance level human parsing via part grouping network"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "relational inductive biases deep learning and graph networks"
    - "learning human object interactions by graph parsing neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "macro micro adversarial network for human parsing"
    - "holistic instance level human parsing"

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"
    - "learning 3d human dynamics from video"

Connected through hyperedge HE-7 (induced by source paper "reasoning visual dialogs with structural and partial observations"):
  with members:
    - "flipdial a generative model for two way visual dialogue"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "learning visual question answering by bootstrapping hard attention"
    - "discriminability objective for training descriptive captions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning human object interactions by graph parsing neural networks"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"

Connected through hyperedge HE-8 (induced by source paper "semantic graph convolutional networks for 3d human pose regression"):
  with members:
    - "cartoonish sketch based face editing in videos using identity deformation transfer"
    - "quantized densely connected u nets for efficient landmark localization"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning to forecast and refine residual motion for image to video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "cr gan learning complete representations for multi view generation"
    - "graph r cnn for scene graph generation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"

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)