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: "tracking with multi level features"
  Abstract: We present a novel formulation of the multiple object tracking problem which integrates low and mid-level features. In particular, we formulate the tracking problem as a quadratic program coupling detections and dense point trajectories. Due to the computational complexity of the initial QP, we propose an approximation by two auxiliary problems, a temporal and spatial association, where the temporal subproblem can be efficiently solved by a linear program and the spatial association by a clustering algorithm. The objective function of the QP is used in order to find the optimal number of clusters, where each cluster ideally represents one person. Evaluation is provided for multiple scenarios, showing the superiority of our method with respect to classic tracking-by-detection methods and also other methods that greedily integrate low-level features.

Connected through hyperedge HE-1 (induced by source paper "learning a neural solver for multiple object tracking"):
  with members:
    - "spatial temporal relation networks for multi object tracking"
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "online multi object tracking with dual matching attention networks"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-2 (induced by source paper "fusion of head and full body detectors for multi object tracking"):
  with members:
    - "binary quadratic programing for online tracking of hundreds of people in extremely crowded scenes"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "mot16 a benchmark for multi object tracking"
    - "a multi cut formulation for joint segmentation and tracking of multiple objects"
    - "performance measures and a data set for multi target multi camera tracking"
    - "re identification of humans in crowds using personal social and environmental constraints"
    - "learning to divide and conquer for online multi target tracking"

Connected through hyperedge HE-3 (induced by source paper "tracking the trackers an analysis of the state of the art in multiple object tracking"):
  with members:
    - "learning optimal parameters for multi target tracking with contextual interactions"
    - "tracklet association by online target specific metric learning and coherent dynamics estimation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "online multi target tracking using recurrent neural networks"
    - "temporal dynamic appearance modeling for online multi person tracking"
    - "learning to divide and conquer for online multi target tracking"
    - "imagenet large scale visual recognition challenge"

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)