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: "deep learning in video multi object tracking a survey"
  Abstract: Abstract   The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video. In recent years, with the rise of Deep Learning, the algorithms that provide a solution to this problem have benefited from the representational power of deep models. This paper provides a comprehensive survey on works that employ Deep Learning models to solve the task of MOT on single-camera videos. Four main steps in MOT algorithms are identified, and an in-depth review of how Deep Learning was employed in each one of these stages is presented. A complete experimental comparison of the presented works on the three MOTChallenge datasets is also provided, identifying a number of similarities among the top-performing methods and presenting some possible future research directions.

Connected through hyperedge HE-1 (induced by source paper "multi object tracking via end to end tracklet searching and ranking"):
  with members:
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "on accurate evaluation of gans for language generation"
    - "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"
    - "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-2 (induced by source paper "cascaded structure tensor framework for robust identification of heavily occluded baggage items from multi vendor x ray scans"):
  with members:
    - "evaluation of a dual convolutional neural network architecture for object wise anomaly detection in cluttered x ray security imagery"
    - "a survey of deep learning based object detection"
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "evaluating the transferability and adversarial discrimination of convolutional neural networks for threat object detection and classification within x ray security imagery"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "chest x ray inpainting with deep generative models"
    - "deep affinity network for multiple object tracking"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-3 (induced by source paper "adaptive binarization based on fuzzy integrals"):
  with members:
    - "road crack detection using deep convolutional neural network and adaptive thresholding"
    - "deepotsu document enhancement and binarization using iterative deep learning"
    - "a selectional auto encoder approach for document image binarization"
    - "deeply supervised salient object detection with short connections"
    - "semantic binary segmentation using convolutional networks without decoders"
    - "convolutional feature masking for joint object and stuff segmentation"

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)