human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bottom up higher resolution networks for multi person pose estimation"):
  Abstract: Bottom-up multi-person pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present a Higher-Resolution Network (HigherHRNet) to learn high-resolution feature pyramids. Equipped with mutli-resolution supervision for training and multiresolution aggregation for inference, the proposed approach is able to solve the scale variation challenge in bottom-up multi-person pose estimation and localize the keypoints, especially for small person, more precisely. The feature pyramid in HigherHRNet consists of the feature map output from HRNet and the upsampled higherresolution one through a transposed convolution. HigherHRNet outperforms the previous best bottom-up method by 2.5% AP for medium person on COCO test-dev, showing its effectiveness in handling scale variation. Furthermore, HigherHRNet achieves a state-of-the-art result of 70.5% AP on COCO test-dev without using refinement or other post-processing techniques, surpassing all existing bottom-up methods. The code and models are available at this https URL.

Includes members:
  Member "deeperlab single shot image parser":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "deep high resolution representation learning for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "cornernet detecting objects as paired keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "simple baselines for human pose estimation and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "revisiting rcnn on awakening the classification power of faster rcnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting the sibling head in object detector")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "searching for efficient multi scale architectures for dense image prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nas count counting by density with neural architecture search")

Question: Please tell me which class the source paper 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)