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 "a top down approach to articulated human pose estimation and tracking"):
  Abstract: Both the tasks of multi-person human pose estimation and pose tracking in videos are quite challenging. Existing methods can be categorized into two groups: top-down and bottom-up approaches. In this paper, following the top-down approach, we aim to build a strong baseline system with three modules: human candidate detector, single-person pose estimator and human pose tracker. Firstly, we choose a generic object detector among state-of-the-art methods to detect human candidates. Then, the cascaded pyramid network is used to estimate the corresponding human pose. Finally, we use a flow-based pose tracker to render keypoint-association across frames, i.e., assigning each human candidate a unique and temporally-consistent id, for the multi-target pose tracking purpose. We conduct extensive ablative experiments to validate various choices of models and configurations. We take part in two ECCV 18 PoseTrack challenges: pose estimation and pose tracking.

Includes members:
  Member "pose flow efficient online pose tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "simple baselines for human pose estimation and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "crowdhuman a benchmark for detecting human in a crowd":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adapted center and scale prediction more stable and more accurate")
  Member "deformable convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "towards accurate multi person pose estimation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "knowledge guided deep fractal neural networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning delicate local representations for multi person pose estimation")
  Member "posetrack a benchmark for human pose estimation and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "cascaded pyramid network for multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "higherhrnet scale aware representation learning for bottom up human 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)