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: "on pairwise costs for network flow multi object tracking"
  Abstract: Multi-object tracking has been recently approached with the min-cost network flow optimization techniques. Such methods simultaneously resolve multiple object tracks in a video and enable modeling of dependencies among tracks. Min-cost network flow methods also fit well within the "tracking-by-detection" paradigm where object trajectories are obtained by connecting per-frame outputs of an object detector. Object detectors, however, often fail due to occlusions and clutter in the video. To cope with such situations, we propose to add pairwise costs to the min-cost network flow framework. While integer solutions to such a problem become NP-hard, we design a convex relaxation solution with an efficient rounding heuristic which empirically gives certificates of small suboptimality. We evaluate two particular types of pairwise costs and demonstrate improvements over recent tracking methods in real-world video sequences.

Connected through hyperedge HE-1 (induced by source paper "unsupervised multiple person tracking using autoencoder based lifted multicuts"):
  with members:
    - "tracking without bells and whistles"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "exploit the connectivity multi object tracking with trackletnet"
    - "unsupervised person re identification by deep learning tracklet association"

Connected through hyperedge HE-2 (induced by source paper "pedestrian models for autonomous driving part i low level models from sensing to tracking"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "features for multi target multi camera tracking and re identification"
    - "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "multi camera multi object tracking"
    - "posetrack joint multi person pose estimation and tracking"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "a review of tracking prediction and decision making methods for autonomous driving"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "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"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"

Connected through hyperedge HE-4 (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-5 (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"
    - "machine learning methods for solving assignment problems in multi target 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"

Connected through hyperedge HE-6 (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"
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "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"

Connected through hyperedge HE-7 (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"
    - "multi target tracking in multiple non overlapping cameras using constrained dominant sets"
    - "working hard to know your neighbor s margins local descriptor learning loss"

Connected through hyperedge HE-8 (induced by source paper "revisiting projection free optimization for strongly convex constraint sets"):
  with members:
    - "introduction to online convex optimization"
    - "following the leader and fast rates in linear prediction curved constraint sets and other regularities"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "robust optimization for deep regression"
    - "projection free online learning"
    - "large scale convex minimization with a low rank constraint"
    - "exact matrix completion via convex optimization"

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)