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: "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
  Abstract: In this study, a multiple hypothesis tracking (MHT) algorithm for multi-target multi-camera tracking (MCT) with disjoint views is proposed. Our method forms track-hypothesis trees, and each branch of them represents a multi-camera track of a target that may move within a camera as well as move across cameras. Furthermore, multi-target tracking within a camera is performed simultaneously with the tree formation by manipulating a status of each track hypothesis. Each status represents three different stages of a multi-camera track: tracking, searching, and end-of-track. The tracking status means targets are tracked by a single camera tracker. In the searching status, the disappeared targets are examined if they reappear in other cameras. The end-of-track status does the target exited the camera network due to its lengthy invisibility. These three status assists MHT to form the track-hypothesis trees for multi-camera tracking. Furthermore, they present a gating technique for eliminating of unlikely observation-to-track association. In the experiments, they evaluate the proposed method using two datasets, DukeMTMC and NLPR-MCT, which demonstrates that the proposed method outperforms the state-of-the-art method in terms of improvement of the accuracy. In addition, they show that the proposed method can operate in real-time and online.

Connected through hyperedge HE-1 (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"
    - "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"
    - "multi hierarchical independent correlation filters for visual tracking"

Connected through hyperedge HE-2 (induced by source paper "locality aware appearance metric for multi target multi camera tracking"):
  with members:
    - "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"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"

Connected through hyperedge HE-3 (induced by source paper "tracking by animation unsupervised learning of multi object attentive trackers"):
  with members:
    - "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"
    - "multi target tracking in multiple non overlapping cameras using constrained dominant sets"
    - "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-4 (induced by source paper "features for multi target multi camera tracking and re identification"):
  with members:
    - "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"
    - "multi target tracking in multiple non overlapping cameras using constrained dominant sets"
    - "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)