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: "sot for mot"
  Abstract: In this paper we present a robust tracker to solve the multiple object tracking (MOT) problem, under the framework of tracking-by-detection. As the first contribution, we innovatively combine single object tracking (SOT) algorithms with multiple object tracking algorithms, and our results show that SOT is a general way to strongly reduce the number of false negatives, regardless of the quality of detection. Another contribution is that we show with a deep learning based appearance model, it is easy to associate detections of the same object efficiently and also with high accuracy. This appearance model plays an important role in our MOT algorithm to correctly associate detections into long trajectories, and also in our SOT algorithm to discover new detections mistakenly missed by the detector. The deep neural network based model ensures the robustness of our tracking algorithm, which can perform data association in a wide variety of scenes. We ran comprehensive experiments on a large-scale and challenging dataset, the MOT16 benchmark, and results showed that our tracker achieved state-of-the-art performance based on both public and private detections.

Connected through hyperedge HE-1 (induced by source paper "graph neural based end to end data association framework for online multiple object tracking"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "relational deep reinforcement learning"
    - "features for multi target multi camera tracking and re identification"
    - "a twofold siamese network for real time object tracking"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "model free tracking with deep appearance and motion features integration"
    - "relational inductive biases deep learning and graph networks"
    - "semi supervised learning on graphs with generative adversarial nets"

Connected through hyperedge HE-2 (induced by source paper "improving online multiple object tracking with deep metric learning"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "simple online and realtime tracking with a deep association metric"
    - "detect to track and track to detect"
    - "end to end comparative attention networks for person re identification"
    - "denet scalable real time object detection with directed sparse sampling"
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "simple online and realtime tracking"

Connected through hyperedge HE-3 (induced by source paper "deep similarity metric learning for real time pedestrian tracking"):
  with members:
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "in defense of the triplet loss for person re identification"
    - "real time multiple object tracking a study on the importance of speed"
    - "simple online and realtime tracking with a deep association metric"
    - "end to end comparative attention networks for person re identification"
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "simple online and realtime tracking"

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)