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: "a multi cut formulation for joint segmentation and tracking of multiple objects"
  Abstract: Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks benefit from decomposing a graphical model into an optimal number of connected components based on attractive and repulsive pairwise terms. The two tasks are formulated on different levels of granularity and, accordingly, leverage mostly local information for motion segmentation and mostly high-level information for multi-target tracking. In this paper we argue that point trajectories and their local relationships can contribute to the high-level task of multi-target tracking and also argue that high-level cues from object detection and tracking are helpful to solve motion segmentation. We propose a joint graphical model for point trajectories and object detections whose Multicuts are solutions to motion segmentation {\it and} multi-target tracking problems at once. Results on the FBMS59 motion segmentation benchmark as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark demonstrate the promise of this joint approach.

Connected through hyperedge HE-1 (induced by source paper "looking glamorous vehicle re id in heterogeneous cameras networks with global and local attention"):
  with members:
    - "impact of fully connected layers on performance of convolutional neural networks for image classification"
    - "part guided attention learning for vehicle re identification"
    - "attributes guided feature learning for vehicle re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"
    - "the state of sparsity in deep neural networks"
    - "group normalization"
    - "ram a region aware deep model for vehicle re identification"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-2 (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-3 (induced by source paper "spatial temporal relation networks for multi object tracking"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "non local neural networks"
    - "attention is all you need"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-4 (induced by source paper "online multi object tracking with dual matching attention networks"):
  with members:
    - "focal loss for dense object detection"
    - "loss max pooling for semantic image segmentation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "residual attention network for image classification"
    - "eco efficient convolution operators for tracking"
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"

Connected through hyperedge HE-5 (induced by source paper "the mutex watershed and its objective efficient parameter free image partitioning"):
  with members:
    - "learned watershed end to end learning of seeded segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "learning steerable filters for rotation equivariant cnns"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "deep watershed transform for instance segmentation"

Connected through hyperedge HE-6 (induced by source paper "online multi object tracking with instance aware tracker and dynamic model refreshment"):
  with members:
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "sanet structure aware network for visual tracking"
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "simple online and realtime tracking"
    - "online multi target tracking using recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "moana an online learned adaptive appearance model for robust multiple object tracking in 3d"):
  with members:
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "a hybrid data association framework for robust online multi object tracking"
    - "tracklet association by online target specific metric learning and coherent dynamics estimation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "mot16 a benchmark for multi object tracking"
    - "online multi target tracking using recurrent neural networks"
    - "performance measures and a data set for multi target multi camera tracking"

Connected through hyperedge HE-8 (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"

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)