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: "2d 3d pose estimation and action recognition using multitask deep learning"
  Abstract: Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature. In this work, we propose a multitask framework for jointly 2D and 3D pose estimation from still images and human action recognition from video sequences. We show that a single architecture can be used to solve the two problems in an efficient way and still achieves state-of-the-art results. Additionally, we demonstrate that optimization from end-to-end leads to significantly higher accuracy than separated learning. The proposed architecture can be trained with data from different categories simultaneously in a seamlessly way. The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate the effectiveness of our method on the targeted tasks.

Connected through hyperedge HE-1 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "learning variations in human motion via mix and match perturbation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "learning trajectory dependencies for human motion prediction"
    - "structured prediction helps 3d human motion modelling"
    - "imitation learning for human pose prediction"
    - "predicting 3d human dynamics from video"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild"):
  with members:
    - "distill knowledge from nrsfm for weakly supervised 3d pose learning"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "learning the depths of moving people by watching frozen people"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"

Connected through hyperedge HE-3 (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"
    - "how robust is 3d human pose estimation to occlusion"
    - "3d human pose estimation in the wild by adversarial learning"
    - "3d human pose estimation with 2d marginal heatmaps"

Connected through hyperedge HE-4 (induced by source paper "lightweight 3d human pose estimation network training using teacher student learning"):
  with members:
    - "learnable triangulation of human pose"
    - "improved training of binary networks for human pose estimation and image recognition"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "deep high resolution representation learning for human pose estimation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "ordinal depth supervision for 3d human pose estimation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

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"
    - "learning 3d human dynamics from video"

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 "human action performance using deep neuro fuzzy recurrent attention model"):
  with members:
    - "a fine to coarse convolutional neural network for 3d human action recognition"
    - "human action recognition and prediction a survey"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "action attending graphic neural network"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "attention is all you need"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "arbee towards automated recognition of bodily expression of emotion in the wild"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "posetrack joint multi person pose estimation and tracking"
    - "simple online and realtime tracking"
    - "semi supervised classification with graph convolutional networks"

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)