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: "human pose estimation with spatial contextual information"
  Abstract: We explore the importance of spatial contextual information in human pose estimation. Most state-of-the-art pose networks are trained in a multi-stage manner and produce several auxiliary predictions for deep supervision. With this principle, we present two conceptually simple and yet computational efficient modules, namely Cascade Prediction Fusion (CPF) and Pose Graph Neural Network (PGNN), to exploit underlying contextual information. Cascade prediction fusion accumulates prediction maps from previous stages to extract informative signals. The resulting maps also function as a prior to guide prediction at following stages. To promote spatial correlation among joints, our PGNN learns a structured representation of human pose as a graph. Direct message passing between different joints is enabled and spatial relation is captured. These two modules require very limited computational complexity. Experimental results demonstrate that our method consistently outperforms previous methods on MPII and LSP benchmark.

Connected through hyperedge HE-1 (induced by source paper "unipose unified human pose estimation in single images and videos"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "cascade feature aggregation for human pose estimation"
    - "deep high resolution representation learning for human pose estimation"
    - "lighttrack a generic framework for online top down human pose tracking"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "rethinking on multi stage networks for human pose estimation"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "multi scale structure aware network for human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "detnet a backbone network for object detection"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "cascade feature aggregation for human pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multi scale structure aware network for human pose estimation"
    - "learning to refine human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "learning feature pyramids for human pose estimation"
    - "human pose regression by combining indirect part detection and contextual information"

Connected through hyperedge HE-4 (induced by source paper "trb a novel triplet representation for understanding 2d human body"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "multi person articulated tracking with spatial and temporal embeddings"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "dense pose transfer"
    - "densepose dense human pose estimation in the wild"
    - "detecting and recognizing human object interactions"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"

Connected through hyperedge HE-5 (induced by source paper "anti confusing region aware network for human pose estimation"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "devil in the details towards accurate single and multiple human parsing"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "multi scale structure aware network for human pose estimation"
    - "learning to refine human pose estimation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"

Connected through hyperedge HE-6 (induced by source paper "global context for convolutional pose machines"):
  with members:
    - "real time 2d multi person pose estimation on cpu lightweight openpose"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "fast human pose estimation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)