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: "multi person pose estimation with enhanced channel wise and spatial information"
  Abstract: Multi-person pose estimation is an important but challenging problem in computer vision. Although current approaches have achieved significant progress by fusing the multi-scale feature maps, they pay little attention to enhancing the channel-wise and spatial information of the feature maps. In this paper, we propose two novel modules to perform the enhancement of the information for the multi-person pose estimation. First, a Channel Shuffle Module (CSM) is proposed to adopt the channel shuffle operation on the feature maps with different levels, promoting cross-channel information communication among the pyramid feature maps. Second, a Spatial, Channel-wise Attention Residual Bottleneck (SCARB) is designed to boost the original residual unit with attention mechanism, adaptively highlighting the information of the feature maps both in the spatial and channel-wise context. The effectiveness of our proposed modules is evaluated on the COCO keypoint benchmark, and experimental results show that our approach achieves the state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "learning delicate local representations for multi person pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "res2net a new multi scale backbone architecture"
    - "omni scale feature learning for person re identification"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "learning a discriminative feature network for semantic segmentation"
    - "multi scale structure aware network for human pose estimation"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-2 (induced by source paper "followmeup sports new benchmark for 2d human keypoint recognition"):
  with members:
    - "semantic graph convolutional networks for 3d human pose regression"
    - "pifpaf composite fields for human pose estimation"
    - "deep high resolution representation learning for human pose estimation"
    - "posefix model agnostic general human pose refinement network"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "real time 2d multi person pose estimation on cpu lightweight openpose"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"

Connected through hyperedge HE-3 (induced by source paper "distribution aware coordinate representation for human pose estimation"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "multi scale structure aware network for human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "learning feature pyramids for human pose estimation"
    - "towards accurate multi person pose estimation in the wild"
    - "compositional human pose regression"
    - "knowledge guided deep fractal neural networks for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "towards good practices for video object segmentation"):
  with members:
    - "video object segmentation using space time memory networks"
    - "mask propagation network for video object segmentation"
    - "youtube vos sequence to sequence video object segmentation"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "towards good practices for multi person pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "posefix model agnostic general human pose refinement network"
    - "associative embedding end to end learning for joint detection and grouping"
    - "deep residual learning for image 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)