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: "long term correlation tracking using multi layer hybrid features in sparse and dense environments"
  Abstract: Tracking a target of interest in both sparse and crowded environments is a challenging problem, not yet successfully addressed in the literature. In this paper, we propose a new long-term visual tracking algorithm, learning discriminative correlation filters and using an online classifier, to track a target of interest in both sparse and crowded video sequences. First, we learn a translation correlation filter using a multi-layer hybrid of convolutional neural networks (CNN) and traditional hand-crafted features. We combine advantages of both the lower convolutional layer which retains more spatial details for precise localization and the higher convolutional layer which encodes semantic information for handling appearance variations, and then integrate these with histogram of oriented gradients (HOG) and color-naming traditional features. Second, we include a re-detection module for overcoming tracking failures due to long-term occlusions by training an incremental (online) SVM on the most confident frames using hand-engineered features. This re-detection module is activated only when the correlation response of the object is below some pre-defined threshold. This generates high score detection proposals which are temporally filtered using a Gaussian mixture probability hypothesis density (GM-PHD) filter to find the detection proposal with the maximum weight as the target state estimate by removing the other detection proposals as clutter. Finally, we learn a scale correlation filter for estimating the scale of a target by constructing a target pyramid around the estimated or re-detected position using the HOG features. We carry out extensive experiments on both sparse and dense data sets which show that our method significantly outperforms state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "occlusion robust online multi object visual tracking using a gm phd filter with a cnn based re identification"):
  with members:
    - "robust online multi target visual tracking using a hisp filter with discriminative deep appearance learning"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "online multiple pedestrian tracking using deep temporal appearance matching association"
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "development of a n type gm phd filter for multiple target multiple type visual tracking"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "region based quality estimation network for large scale person re identification"
    - "person transfer gan to bridge domain gap for person re identification"

Connected through hyperedge HE-2 (induced by source paper "robust online multi target visual tracking using a hisp filter with discriminative deep appearance learning"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "development of a n type gm phd filter for multiple target multiple type visual tracking"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "focal loss for dense object detection"
    - "region based quality estimation network for large scale person re identification"
    - "person transfer gan to bridge domain gap for person re identification"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "mot16 a benchmark for multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "development of a n type gm phd filter for multiple target multiple type visual tracking"):
  with members:
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "learning to detect vehicles by clustering appearance patterns"
    - "near online multi target tracking with aggregated local flow descriptor"
    - "multiple object tracking a review"
    - "rich feature hierarchies for accurate object detection and semantic 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)