human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "globally consistent multi people tracking using motion patterns"
  Abstract: Many state-of-the-art approaches to people tracking rely on detecting them in each frame independently, grouping detections into short but reliable trajectory segments, and then further grouping them into full trajectories. This grouping typically relies on imposing local smoothness constraints but almost never on enforcing more global constraints on the trajectories. In this paper, we propose an approach to imposing global consistency by first inferring behavioral patterns from the ground truth and then using them to guide the tracking algorithm. When used in conjunction with several state-of-the-art algorithms, this further increases their already good performance. Furthermore, we propose an unsupervised scheme that yields almost similar improvements without the need for ground truth.

Connected through hyperedge HE-1 (induced by source paper "online visual robot tracking and identification using deep lstm networks"):
  with members:
    - "a monocular vision system for playing soccer in low color information environments"
    - "real time visual tracking and identification for a team of homogeneous humanoid robots"
    - "child sized 3d printed igus humanoid open platform"
    - "lstm a search space odyssey"
    - "robust and real time deep tracking via multi scale domain adaptation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "end to end tracking and semantic segmentation using recurrent neural networks"
    - "deep tracking seeing beyond seeing using recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"):
  with members:
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "pathtrack fast trajectory annotation with path supervision"
    - "deep network flow for multi object tracking"
    - "mot16 a benchmark for multi object tracking"
    - "performance measures and a data set for multi target multi camera tracking"
    - "wide residual networks"
    - "temporal dynamic appearance modeling for online multi person tracking"

Connected through hyperedge HE-3 (induced by source paper "a hybrid data association framework for robust online multi object tracking"):
  with members:
    - "tracklet association by online target specific metric learning and coherent dynamics estimation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"
    - "temporal dynamic appearance modeling for online multi person tracking"
    - "multi person tracking by multicut and deep matching"
    - "visualizing and understanding recurrent networks"
    - "learning to divide and conquer for online multi target tracking"

Question: Please tell me which class the center node 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)