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: "joint group feature selection and discriminative filter learning for robust visual object tracking"
  Abstract: We propose a new Group Feature Selection method for Discriminative Correlation Filters (GFS-DCF) based visual object tracking. The key innovation of the proposed method is to perform group feature selection across both channel and spatial dimensions, thus to pinpoint the structural relevance of multi-channel features to the filtering system. In contrast to the widely used spatial regularisation or feature selection methods, to the best of our knowledge, this is the first time that channel selection has been advocated for DCF-based tracking. We demonstrate that our GFS-DCF method is able to significantly improve the performance of a DCF tracker equipped with deep neural network features. In addition, our GFS-DCF enables joint feature selection and filter learning, achieving enhanced discrimination and interpretability of the learned filters. #R##N#To further improve the performance, we adaptively integrate historical information by constraining filters to be smooth across temporal frames, using an efficient low-rank approximation. By design, specific temporal-spatial-channel configurations are dynamically learned in the tracking process, highlighting the relevant features, and alleviating the performance degrading impact of less discriminative representations and reducing information redundancy. The experimental results obtained on OTB2013, OTB2015, VOT2017, VOT2018 and TrackingNet demonstrate the merits of our GFS-DCF and its superiority over the state-of-the-art trackers. The code is publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "multi drone based single object tracking with agent sharing network"):
  with members:
    - "vision meets drones a challenge"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "features for multi target multi camera tracking and re identification"
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"

Connected through hyperedge HE-2 (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion"):
  with members:
    - "an accelerated correlation filter tracker"
    - "infrared and visible image fusion with resnet and zero phase component analysis"
    - "densefuse a fusion approach to infrared and visible images"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "rgb t object tracking benchmark and baseline"
    - "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability"
    - "infrared and visible image fusion using a deep learning framework"
    - "image fusion with cosparse analysis operator"

Connected through hyperedge HE-3 (induced by source paper "tracklinic diagnosis of challenge factors in visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "roi pooled correlation filters for visual tracking"
    - "skimming perusal tracking a framework for real time and robust long term tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"
    - "siamese cascaded region proposal networks for real time visual tracking"

Connected through hyperedge HE-4 (induced by source paper "an accelerated correlation filter tracker"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "deep visual attention prediction"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "video salient object detection via fully convolutional networks"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "crest convolutional residual learning for visual tracking"
    - "learning background aware correlation filters for visual tracking"

Connected through hyperedge HE-5 (induced by source paper "robust visual tracking via implicit low rank constraints and structural color histograms"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "large margin object tracking with circulant feature maps"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"
    - "beyond local search tracking objects everywhere with instance specific proposals"
    - "object detection from video tubelets with convolutional neural networks"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning spatially regularized correlation filters for visual tracking"

Connected through hyperedge HE-6 (induced by source paper "low rank discriminative least squares regression for image classification"):
  with members:
    - "locality constraint dictionary learning with support vector for pattern classification"
    - "densefuse a fusion approach to infrared and visible images"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-7 (induced by source paper "adaptive distraction context aware tracking based on correlation filter"):
  with members:
    - "an accelerated correlation filter tracker"
    - "learning support correlation filters for visual tracking"
    - "high speed tracking with kernelized correlation filters"

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)