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: "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
  Abstract: Multi-Object Tracking (MOT) is a challenging task in the complex scene such as surveillance and autonomous driving. In this paper, we propose a novel tracklet processing method to cleave and re-connect tracklets on crowd or long-term occlusion by Siamese Bi-Gated Recurrent Unit (GRU). The tracklet generation utilizes object features extracted by CNN and RNN to create the high-confidence tracklet candidates in sparse scenario. Due to mis-tracking in the generation process, the tracklets from different objects are split into several sub-tracklets by a bidirectional GRU. After that, a Siamese GRU based tracklet re-connection method is applied to link the sub-tracklets which belong to the same object to form a whole trajectory. In addition, we extract the tracklet images from existing MOT datasets and propose a novel dataset to train our networks. The proposed dataset contains more than 95160 pedestrian images. It has 793 different persons in it. On average, there are 120 images for each person with positions and sizes. Experimental results demonstrate the advantages of our model over the state-of-the-art methods on MOT16.

Connected through hyperedge HE-1 (induced by source paper "multiple object tracking by flowing and fusing"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "instance aware representation learning and association for online multi person tracking"
    - "tracking without bells and whistles"
    - "online multi object tracking with dual matching attention networks"
    - "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"
    - "towards multi object detection and tracking in urban scenario under uncertainties"

Connected through hyperedge HE-2 (induced by source paper "multi object tracking via end to end tracklet searching and ranking"):
  with members:
    - "deep learning in video multi object tracking a survey"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "on accurate evaluation of gans for language generation"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "tracklet association tracker an end to end learning based association approach for multi object tracking"
    - "evaluating text gans as language models"
    - "unsupervised person re identification by deep learning tracklet association"
    - "attention is all you need"

Connected through hyperedge HE-3 (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"
    - "exploit the connectivity multi object tracking with trackletnet"
    - "unsupervised person re identification by deep learning tracklet association"
    - "fusion of head and full body detectors for multi object tracking"

Connected through hyperedge HE-4 (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-5 (induced by source paper "how to train your deep multi object tracker"):
  with members:
    - "tracking without bells and whistles"
    - "mots multi object tracking and segmentation"
    - "online multi object tracking with dual matching attention networks"
    - "combined image and world space tracking in traffic scenes"
    - "distractor aware siamese networks for visual object tracking"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "tracking without bells and whistles"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "features for multi target multi camera tracking and re identification"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "in defense of the triplet loss for person re identification"
    - "an implementation of faster rcnn with study for region sampling"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"

Connected through hyperedge HE-7 (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"
    - "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"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"

Connected through hyperedge HE-8 (induced by source paper "learning a neural solver for multiple object tracking"):
  with members:
    - "spatial temporal relation networks for multi object tracking"
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "online multi object tracking with dual matching attention networks"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "relational inductive biases deep learning and graph networks"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"

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)