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: "learning to divide and conquer for online multi target tracking"
  Abstract: Online Multiple Target Tracking (MTT) is often addressed within the tracking-by-detection paradigm. Detections are previously extracted independently in each frame and then objects trajectories are built by maximizing specifically designed coherence functions. Nevertheless, ambiguities arise in presence of occlusions or detection errors. In this paper we claim that the ambiguities in tracking could be solved by a selective use of the features, by working with more reliable features if possible and exploiting a deeper representation of the target only if necessary. To this end, we propose an online divide and conquer tracker for static camera scenes, which partitions the assignment problem in local subproblems and solves them by selectively choosing and combining the best features. The complete framework is cast as a structural learning task that unifies these phases and learns tracker parameters from examples. Experiments on two different datasets highlights a significant improvement of tracking performances (MOTA +10%) over the state of the art.

Connected through hyperedge HE-1 (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-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 "deep hierarchical machine a flexible divide and conquer architecture"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "deep regression forests for age estimation"
    - "a fast and accurate unconstrained face detector"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "local binary convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "fusion of head and full body detectors for multi object tracking"):
  with members:
    - "binary quadratic programing for online tracking of hundreds of people in extremely crowded scenes"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "mot16 a benchmark for multi object tracking"
    - "a multi cut formulation for joint segmentation and tracking of multiple objects"
    - "performance measures and a data set for multi target multi camera tracking"
    - "tracking with multi level features"
    - "re identification of humans in crowds using personal social and environmental constraints"

Connected through hyperedge HE-5 (induced by source paper "a hybrid data association framework for robust online multi object tracking"):
  with members:
    - "tracklet association by online target specific metric learning and coherent dynamics estimation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "globally consistent multi people tracking using motion patterns"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"
    - "temporal dynamic appearance modeling for online multi person tracking"
    - "multi person tracking by multicut and deep matching"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-6 (induced by source paper "deep network flow for multi object tracking"):
  with members:
    - "learning optimal parameters for multi target tracking with contextual interactions"
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "atgv net accurate depth super resolution"
    - "deep residual learning for image recognition"
    - "motchallenge 2015 towards a benchmark for multi target tracking"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "near online multi target tracking with aggregated local flow descriptor"

Connected through hyperedge HE-7 (induced by source paper "tracking the trackers an analysis of the state of the art in multiple object tracking"):
  with members:
    - "learning optimal parameters for multi target tracking with contextual interactions"
    - "tracklet association by online target specific metric learning and coherent dynamics estimation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "online multi target tracking using recurrent neural networks"
    - "tracking with multi level features"
    - "temporal dynamic appearance modeling for online multi person tracking"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "context aware trajectory prediction"):
  with members:
    - "modeling cooperative navigation in dense human crowds"
    - "deep learning driven visual path prediction from a single image"
    - "what happens if learning to predict the effect of forces in images"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "knowledge transfer for scene specific motion prediction"
    - "forecasting interactive dynamics of pedestrians with fictitious play"

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)