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: "recklessly approximate sparse coding"
  Abstract: It has recently been observed that certain extremely simple feature encoding techniques are able to achieve state of the art performance on several standard image classification benchmarks including deep belief networks, convolutional nets, factored RBMs, mcRBMs, convolutional RBMs, sparse autoencoders and several others. Moreover, these "triangle" or "soft threshold" encodings are ex- tremely efficient to compute. Several intuitive arguments have been put forward to explain this remarkable performance, yet no mathematical justification has been offered. #R##N#The main result of this report is to show that these features are realized as an approximate solution to the a non-negative sparse coding problem. Using this connection we describe several variants of the soft threshold features and demonstrate their effectiveness on two image classification benchmark tasks.

Connected through hyperedge HE-1 (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-2 (induced by source paper "towards understanding sparse filtering a theoretical perspective"):
  with members:
    - "a modular theory of feature learning"
    - "a survey of sparse representation algorithms and applications"
    - "mathematics of sparsity and entropy axioms core functions and sparse recovery"
    - "compute less to get more using orc to improve sparse filtering"
    - "representation learning a review and new perspectives"
    - "comparing measures of sparsity"

Connected through hyperedge HE-3 (induced by source paper "mathbf d 3 deep dual domain based fast restoration of jpeg compressed images"):
  with members:
    - "self tuned deep super resolution"
    - "compression artifacts reduction by a deep convolutional network"
    - "deep networks for image super resolution with sparse prior"
    - "convolutional neural networks at constrained time cost"
    - "deep unfolding model based inspiration of novel deep architectures"

Connected through hyperedge HE-4 (induced by source paper "dictionary learning for fast classification based on soft thresholding"):
  with members:
    - "sparse subspace clustering algorithm theory and applications"
    - "representation learning a review and new perspectives"
    - "fast inference in sparse coding algorithms with applications to object recognition"

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.LG(Machine Learning)