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: "multiple object tracking with kernelized correlation filters in urban mixed traffic"
  Abstract: Recently, the Kernelized Correlation Filters tracker (KCF) achieved competitive performance and robustness in visual object tracking. On the other hand, visual trackers are not typically used in multiple object tracking. In this paper, we investigate how a robust visual tracker like KCF can improve multiple object tracking. Since KCF is a fast tracker, many can be used in parallel and still result in fast tracking. We build a multiple object tracking system based on KCF and background subtraction. Background subtraction is applied to extract moving objects and get their scale and size in combination with KCF outputs, while KCF is used for data association and to handle fragmentation and occlusion problems. As a result, KCF and background subtraction help each other to take tracking decision at every frame. Sometimes KCF outputs are the most trustworthy (e.g. during occlusion), while in some other case, it is the background subtraction outputs. To validate the effectiveness of our system, the algorithm is demonstrated on four urban video recordings from a standard dataset. Results show that our method is competitive with state-of-the-art trackers even if we use a much simpler data association step.

Connected through hyperedge HE-1 (induced by source paper "tracking road users using constraint programming"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "multiple object tracking in urban traffic scenes with a multiclass object detector"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "performance measures and a data set for multi target multi camera tracking"
    - "high speed tracking with kernelized correlation filters"
    - "motchallenge 2015 towards a benchmark for multi target tracking"
    - "ua detrac a new benchmark and protocol for multi object detection and tracking"

Connected through hyperedge HE-2 (induced by source paper "tracking in urban traffic scenes from background subtraction and object detection"):
  with members:
    - "improving multiple object tracking with optical flow and edge preprocessing"
    - "multiple object tracking in urban traffic scenes with a multiclass object detector"
    - "mot16 a benchmark for multi object tracking"
    - "simple online and realtime tracking"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-3 (induced by source paper "machine learning methods for solving assignment problems in multi target tracking"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "combined image and world space tracking in traffic scenes"
    - "graph embedding techniques applications and performance a survey"
    - "carla an open urban driving simulator"
    - "learning optimal parameters for multi target tracking with contextual interactions"
    - "a hybrid data association framework for robust online multi object tracking"
    - "deep reinforcement learning a brief survey"
    - "fusion of head and full body detectors for multi object 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)