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: "towards a principled integration of multi camera re identification and tracking through optimal bayes filters"
  Abstract: With the rise of end-to-end learning through deep learning, person detectors and re-identification (ReID) models have recently become very strong. Multi-camera multi-target (MCMT) tracking has not fully gone through this transformation yet. We intend to take another step in this direction by presenting a theoretically principled way of integrating ReID with tracking formulated as an optimal Bayes filter. This conveniently side-steps the need for data-association and opens up a direct path from full images to the core of the tracker. While the results are still sub-par, we believe that this new, tight integration opens many interesting research opportunities and leads the way towards full end-to-end tracking from raw pixels.

Connected through hyperedge HE-1 (induced by source paper "mots multi object tracking and segmentation"):
  with members:
    - "panoptic segmentation"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "how transferable are the datasets collected by active learners"
    - "practical obstacles to deploying active learning"
    - "combined image and world space tracking in traffic scenes"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-2 (induced by source paper "graph neural based end to end data association framework for online multiple object tracking"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "relational deep reinforcement learning"
    - "features for multi target multi camera tracking and re identification"
    - "a twofold siamese network for real time object tracking"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "model free tracking with deep appearance and motion features integration"
    - "relational inductive biases deep learning and graph networks"
    - "semi supervised learning on graphs with generative adversarial nets"

Connected through hyperedge HE-3 (induced by source paper "learning a spatio temporal embedding for video instance segmentation"):
  with members:
    - "video instance segmentation"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "mots multi object tracking and segmentation"
    - "maskrnn instance level video object segmentation"
    - "path aggregation network for instance segmentation"
    - "tracking emerges by colorizing videos"
    - "recurrent pixel embedding for instance grouping"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-4 (induced by source paper "end to end learning deep crf models for multi object tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "online multi object tracking with dual matching attention networks"
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "multiple target tracking by learning feature representation and distance metric jointly"
    - "in defense of the triplet loss for person re identification"
    - "tracklet association by online target specific metric learning and coherent dynamics estimation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"

Connected through hyperedge HE-5 (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"
    - "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)