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 "multi scale structure aware network for human pose estimation"):
  Abstract: We develop a robust multi-scale structure-aware neural network for human pose estimation. This method improves the recent deep conv-deconv hourglass models with four key improvements: (1) multi-scale supervision to strengthen contextual feature learning in matching body keypoints by combining feature heatmaps across scales, (2) multi-scale regression network at the end to globally optimize the structural matching of the multi-scale features, (3) structure-aware loss used in the intermediate supervision and at the regression to improve the matching of keypoints and respective neighbors to infer a higher-order matching configurations, and (4) a keypoint masking training scheme that can effectively fine-tune our network to robustly localize occluded keypoints via adjacent matches. Our method can effectively improve state-of-the-art pose estimation methods that suffer from difficulties in scale varieties, occlusions, and complex multi-person scenarios. This multi-scale supervision tightly integrates with the regression network to effectively (i) localize keypoints using the ensemble of multi-scale features, and (ii) infer global pose configuration by maximizing structural consistencies across multiple keypoints and scales. The keypoint masking training enhances these advantages to focus learning on hard occlusion samples. Our method achieves the leading position in the MPII challenge leaderboard among the state-of-the-art methods.

Includes members:
  Member "learning feature pyramids for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "self adversarial training for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation")
  Member "dual path networks for multi person human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pose estimator and tracker using temporal flow maps for limbs")
  Member "human pose regression by combining indirect part detection and contextual information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "kpnet towards minimal face detector")
  Member "adversarial posenet a structure aware convolutional network for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lightweight 3d human pose estimation network training using teacher student learning")
  Member "multi context attention for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "diversified visual attention networks for fine grained object classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way")
  Member "convolutional pose machines":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")

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)