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 "panda a gigapixel level human centric video dataset"):
  Abstract: We present PANDA, the first gigaPixel-level humAN-centric viDeo dAtaset, for large-scale, long-term, and multi-object visual analysis. The videos in PANDA were captured by a gigapixel camera and cover real-world scenes with both wide field-of-view (~1 square kilometer area) and high-resolution details (~gigapixel-level/frame). The scenes may contain 4k head counts with over 100x scale variation. PANDA provides enriched and hierarchical ground-truth annotations, including 15,974.6k bounding boxes, 111.8k fine-grained attribute labels, 12.7k trajectories, 2.2k groups and 2.9k interactions. We benchmark the human detection and tracking tasks. Due to the vast variance of pedestrian pose, scale, occlusion and trajectory, existing approaches are challenged by both accuracy and efficiency. Given the uniqueness of PANDA with both wide FoV and high resolution, a new task of interaction-aware group detection is introduced. We design a 'global-to-local zoom-in' framework, where global trajectories and local interactions are simultaneously encoded, yielding promising results. We believe PANDA will contribute to the community of artificial intelligence and praxeology by understanding human behaviors and interactions in large-scale real-world scenes. PANDA Website: this http URL.

Includes members:
  Member "eurocity persons a novel benchmark for person detection in traffic scenes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adapted center and scale prediction more stable and more accurate")
  Member "cvpr19 tracking and detection challenge how crowded can it get":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "virtual to real adaptation of pedestrian detectors for smart cities")
  Member "collaborative global local networks for memory efficient segmentation of ultra high resolution images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "model compression with adversarial robustness a unified optimization framework")
  Member "widerperson a diverse dataset for dense pedestrian detection in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ps rcnn detecting secondary human instances in a crowd via primary object suppression")
  Member "group reidentification with multigrained matching and integration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep set to set matching and learning")
  Member "mmdetection open mmlab detection toolbox and benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "vision meets drones a challenge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos")
  Member "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sequential forecasting of 100 000 points")

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)