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: "latent constrained correlation filter"
  Abstract: Correlation filters are special classifiers designed for shift-invariant object recognition, which are robust to pattern distortions. The recent literature shows that combining a set of sub-filters trained based on a single or a small group of images obtains the best performance. The idea is equivalent to estimating variable distribution based on the data sampling (bagging), which can be interpreted as finding solutions (variable distribution approximation) directly from sampled data space. However, this methodology fails to account for the variations existed in the data. In this paper, we introduce an intermediate step—solution sampling—after the data sampling step to form a subspace, in which an optimal solution can be estimated. More specifically, we propose a new method, named latent constrained correlation filters (LCCF), by mapping the correlation filters to a given latent subspace, and develop a new learning framework in the latent subspace that embeds distribution-related constraints into the original problem. To solve the optimization problem, we introduce a subspace-based alternating direction method of multipliers, which is proven to converge at the saddle point. Our approach is successfully applied to three different tasks, including eye localization, car detection, and object tracking. Extensive experiments demonstrate that LCCF outperforms the state-of-the-art methods. 1    1  The source code will be publicly available.  https://github.com/bczhangbczhang/

Connected through hyperedge HE-1 (induced by source paper "aggregation signature for small object tracking"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "state aware anti drift object tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "the world of fast moving objects"
    - "robust estimation of similarity transformation for visual object tracking"
    - "eco efficient convolution operators for tracking"
    - "discriminative scale space tracking"
    - "learning spatially regularized correlation filters for visual tracking"

Connected through hyperedge HE-2 (induced by source paper "gabor convolutional networks"):
  with members:
    - "one two one networks for compression artifacts reduction in remote sensing"
    - "gabor filter assisted energy efficient fast learning convolutional neural networks"
    - "oriented response networks"
    - "deformable convolutional networks"
    - "structured receptive fields in cnns"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "wide residual networks"
    - "local binary convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "object detection and tracking benchmark in industry based on improved correlation filter"):
  with members:
    - "one two one networks for compression artifacts reduction in remote sensing"
    - "high speed tracking with kernelized correlation filters"
    - "correlation filters with limited boundaries"

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)