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: "dictionary learning for fast classification based on soft thresholding"
  Abstract: Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits the applicability of these methods in large-scale problems, or in scenarios where computational power is restricted. We consider in this paper a simple yet efficient alternative to sparse coding for feature extraction. We study a classification scheme that applies the soft-thresholding nonlinear mapping in a dictionary, followed by a linear classifier. A novel supervised dictionary learning algorithm tailored for this low complexity classification architecture is proposed. The dictionary learning problem, which jointly learns the dictionary and linear classifier, is cast as a difference of convex (DC) program and solved efficiently with an iterative DC solver. We conduct experiments on several datasets, and show that our learning algorithm that leverages the structure of the classification problem outperforms generic learning procedures. Our simple classifier based on soft-thresholding also competes with the recent sparse coding classifiers, when the dictionary is learned appropriately. The adopted classification scheme further requires less computational time at the testing stage, compared to other classifiers. The proposed scheme shows the potential of the adequately trained soft-thresholding mapping for classification and paves the way towards the development of very efficient classification methods for vision problems.

Connected through hyperedge HE-1 (induced by source paper "revisiting hard thresholding for dnn pruning"):
  with members:
    - "fast convex pruning of deep neural networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "compressing neural networks using the variational information bottleneck"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "class specific or shared a hybrid dictionary learning network for image classification"):
  with members:
    - "rethinking feature distribution for loss functions in image classification"
    - "between class learning for image classification"
    - "deep residual learning for image recognition"
    - "pcanet a simple deep learning baseline for image classification"

Connected through hyperedge HE-3 (induced by source paper "learning simple thresholded features with sparse support recovery"):
  with members:
    - "cross domain visual recognition via domain adaptive dictionary learning"
    - "a fast and accurate system for face detection identification and verification"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "deep regionlets blended representation and deep learning for generic object detection"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "deep residual learning for image recognition"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-4 (induced by source paper "discriminative nonlinear analysis operator learning when cosparse model meets image classification"):
  with members:
    - "joint sparse recovery with semisupervised music"
    - "sparse modeling for image and vision processing"
    - "analysis operator learning and its application to image reconstruction"
    - "representation learning a review and new perspectives"
    - "fast approximations to structured sparse coding and applications to object classification"

Connected through hyperedge HE-5 (induced by source paper "computational cost reduction in learned transform classifications"):
  with members:
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep learning in neural networks"
    - "neural networks with few multiplications"
    - "deep learning with limited numerical precision"
    - "training deep neural networks with low precision multiplications"
    - "representation learning a review and new perspectives"

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)