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: "machine learning methods for solving assignment problems in multi target tracking"
  Abstract: Data association and track-to-track association, two fundamental problems in single-sensor and multi-sensor multi-target tracking, are instances of an NP-hard combinatorial optimization problem known as the multidimensional assignment problem (MDAP). Over the last few years, data-driven approaches to tackling MDAPs in tracking have become increasingly popular. We argue that viewing multi-target tracking as an assignment problem conceptually unifies the wide variety of machine learning methods that have been proposed for data association and track-to-track association. In this survey, we review recent literature, provide rigorous formulations of the assignment problems encountered in multi-target tracking, and review classic approaches used prior to the shift towards data-driven techniques. Recent attempts at using deep learning to solve NP-hard combinatorial optimization problems, including data association, are discussed as well. We highlight representation learning methods for multi-sensor applications and conclude by providing an overview of current multi-target tracking benchmarks.

Connected through hyperedge HE-1 (induced by source paper "occlusion robust online multi object visual tracking using a gm phd filter with a cnn based re identification"):
  with members:
    - "robust online multi target visual tracking using a hisp filter with discriminative deep appearance learning"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "online multiple pedestrian tracking using deep temporal appearance matching association"
    - "development of a n type gm phd filter for multiple target multiple type visual tracking"
    - "long term correlation tracking using multi layer hybrid features in sparse and dense environments"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "region based quality estimation network for large scale person re identification"
    - "person transfer gan to bridge domain gap for person re identification"

Connected through hyperedge HE-2 (induced by source paper "efficient global multi object tracking under minimum cost circulation framework"):
  with members:
    - "end to end learning deep crf models for multi object tracking"
    - "a baseline for 3d multi object tracking"
    - "online multiple pedestrian tracking using deep temporal appearance matching association"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "fusion of head and full body detectors for multi object tracking"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "deep network flow for multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "synaptic partner assignment using attentional voxel association networks"):
  with members:
    - "detecting synapse location and connectivity by signed proximity estimation and pruning with deep nets"
    - "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"
    - "synaptic partner prediction from point annotations in insect brains"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "an error detection and correction framework for connectomics"
    - "toward streaming synapse detection with compositional convnets"

Connected through hyperedge HE-4 (induced by source paper "deepda lstm based deep data association network for multi targets tracking in clutter"):
  with members:
    - "deep recurrent neural network for multi target filtering"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep affinity network for multiple object tracking"):
  with members:
    - "learning non uniform hypergraph for multi object tracking"
    - "deep cosine metric learning for person re identification"
    - "benchmark data and method for real time people counting in cluttered scenes using depth sensors"
    - "learning regression and verification networks for long term visual tracking"
    - "learning optimal parameters for multi target tracking with contextual interactions"
    - "fusion of head and full body detectors for multi object tracking"
    - "detect to track and track to detect"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"

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)