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 object tracking with multiple cues and switcher aware classification"):
  Abstract: In this paper, we propose a unified Multi-Object Tracking (MOT) framework learning to make full use of long term and short term cues for handling complex cases in MOT scenes. Besides, for better association, we propose switcher-aware classification (SAC), which takes the potential identity-switch causer (switcher) into consideration. Specifically, the proposed framework includes a Single Object Tracking (SOT) sub-net to capture short term cues, a re-identification (ReID) sub-net to extract long term cues and a switcher-aware classifier to make matching decisions using extracted features from the main target and the switcher. Short term cues help to find false negatives, while long term cues avoid critical mistakes when occlusion happens, and the SAC learns to combine multiple cues in an effective way and improves robustness. The method is evaluated on the challenging MOT benchmarks and achieves the state-of-the-art results.

Includes members:
  Member "online multi object tracking with dual matching attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "online multi object tracking with historical appearance matching and scene adaptive detection filtering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "real time multiple people tracking with deeply learned candidate selection and person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "fusion of head and full body detectors for multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "simple online and realtime tracking with a deep association metric":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "tracking the untrackable learning to track multiple cues with long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi object tracking via end to end tracklet searching and ranking")
  Member "eco efficient convolution operators for tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")

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)