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: "multi target tracking in multiple non overlapping cameras using constrained dominant sets"
  Abstract: In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant sets clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to solve both tracking tasks. The tracking problem is caste as finding constrained dominant sets from a graph. In addition to having a unified framework that simultaneously solves within- and across-camera tracking, the third layer helps link broken tracks of the same person occurring during within-camera tracking. In this work, we propose a fast algorithm, based on dynamics from evolutionary game theory, which is efficient and salable to large-scale real-world applications.

Connected through hyperedge HE-1 (induced by source paper "constrained dominant sets and its applications in computer vision"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "deep constrained dominant sets for person re identification"
    - "weakly supervised semantic segmentation using constrained dominant sets"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning neural models for end to end clustering"
    - "transductive label augmentation for improved deep network learning"
    - "multi feature fusion for image retrieval using constrained dominant sets"

Connected through hyperedge HE-2 (induced by source paper "state aware re identification feature for multi target multi camera tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "learning non uniform hypergraph for multi object tracking"
    - "features for multi target multi camera tracking and re identification"
    - "distractor aware siamese networks for visual object tracking"

Connected through hyperedge HE-3 (induced by source paper "locality aware appearance metric for multi target multi camera tracking"):
  with members:
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "features for multi target multi camera tracking and re identification"
    - "improving online multiple object tracking with deep metric learning"
    - "multiple target tracking by learning feature representation and distance metric jointly"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"

Connected through hyperedge HE-4 (induced by source paper "deep constrained dominant sets for person re identification"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "harmonious attention network for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "multi feature fusion for image retrieval using constrained dominant sets"
    - "deep group shuffling random walk for person re identification"

Connected through hyperedge HE-5 (induced by source paper "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"):
  with members:
    - "convolutional pose machines"
    - "performance measures and a data set for multi target multi camera tracking"

Connected through hyperedge HE-6 (induced by source paper "egoreid dataset person re identification in videos acquired by mobile devices with first person point of view"):
  with members:
    - "unsupervised traffic accident detection in first person videos"
    - "scaling egocentric vision the epic kitchens dataset"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "person search with natural language description"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-7 (induced by source paper "tracking by animation unsupervised learning of multi object attentive trackers"):
  with members:
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "features for multi target multi camera tracking and re identification"
    - "learning to decompose and disentangle representations for video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "neural expectation maximization"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "deep network flow for multi object tracking"
    - "deep tracking seeing beyond seeing using recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "features for multi target multi camera tracking and re identification"):
  with members:
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "towards a principled integration of multi camera re identification and tracking through optimal bayes filters"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "multi target multi camera tracking by hierarchical clustering recent progress on dukemtmc project"

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)