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: "shift invariance sparse coding for audio classification"
  Abstract: Sparse coding is an unsupervised learning algorithm that learns a succinct high-level representation of the inputs given only unlabeled data; it represents each input as a sparse linear combination of a set of basis functions. Originally applied to modeling the human visual cortex, sparse coding has also been shown to be useful for self-taught learning, in which the goal is to solve a supervised classification task given access to additional unlabeled data drawn from different classes than that in the supervised learning problem. Shift-invariant sparse coding (SISC) is an extension of sparse coding which reconstructs a (usually time-series) input using all of the basis functions in all possible shifts. In this paper, we present an efficient algorithm for learning SISC bases. Our method is based on iteratively solving two large convex optimization problems: The first, which computes the linear coefficients, is an L1-regularized linear least squares problem with potentially hundreds of thousands of variables. Existing methods typically use a heuristic to select a small subset of the variables to optimize, but we present a way to efficiently compute the exact solution. The second, which solves for bases, is a constrained linear least squares problem. By optimizing over complex-valued variables in the Fourier domain, we reduce the coupling between the different variables, allowing the problem to be solved efficiently. We show that SISC's learned high-level representations of speech and music provide useful features for classification tasks within those domains. When applied to classification, under certain conditions the learned features outperform state of the art spectral and cepstral features.

Connected through hyperedge HE-1 (induced by source paper "deep dictionary learning a parametric network approach"):
  with members:
    - "connections between nuclear norm and frobenius norm based representations"
    - "structured analysis dictionary learning for image classification"
    - "fast low rank shared dictionary learning for image classification"
    - "opening the black box of deep neural networks via information"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "universal adversarial perturbations"
    - "discriminative bayesian dictionary learning for classification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"):
  with members:
    - "training neural audio classifiers with few data"
    - "prototypical networks for few shot learning"
    - "transfer learning for speech recognition on a budget"
    - "honk a pytorch reimplementation of convolutional neural networks for keyword spotting"
    - "transfer learning for music classification and regression tasks"
    - "matching networks for one shot learning"
    - "deep residual learning for image recognition"
    - "deep speech scaling up end to end speech recognition"

Connected through hyperedge HE-3 (induced by source paper "on learning with shift invariant structures"):
  with members:
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "dictionary learning for adaptive gpr target classification"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "optimization methods for convolutional sparse coding"
    - "compressive shift retrieval"
    - "circulant and toeplitz matrices in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "an efficient privacy preserving algorithm based on randomized response in iot based smart grid"):
  with members:
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "toward practical differential privacy in smart grid with capacity limited rechargeable batteries"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "deep image prior"):
  with members:
    - "latent convolutional models"
    - "phase retrieval for fourier ptychography under varying amount of measurements"
    - "photorealistic image reconstruction from hybrid intensity and event based sensor"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "convolutional dictionary learning via local processing"
    - "benchmarking denoising algorithms with real photographs"
    - "the robust manifold defense adversarial training using generative models"
    - "zero shot super resolution using deep internal learning"

Connected through hyperedge HE-6 (induced by source paper "enhanced signal recovery via sparsity inducing image priors"):
  with members:
    - "sparsity based color image super resolution via exploiting cross channel constraints"
    - "deep convolutional neural network for inverse problems in imaging"
    - "accelerating the super resolution convolutional neural network"
    - "detecting dominant vanishing points in natural scenes with application to composition sensitive image retrieval"
    - "screen content image segmentation using sparse smooth decomposition"
    - "accurate image super resolution using very deep convolutional networks"
    - "self tuned deep super resolution"
    - "dfdl discriminative feature oriented dictionary learning for histopathological image classification"

Connected through hyperedge HE-7 (induced by source paper "on the global local dichotomy in sparsity modeling"):
  with members:
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "local sparsity and recovery of fusion frames structured signals"
    - "working locally thinking globally part ii stability and algorithms for convolutional sparse coding"
    - "approximation vector machines for large scale online learning"
    - "structured sparsity discrete and convex approaches"
    - "boosting of image denoising algorithms"
    - "sparse modeling for image and vision processing"
    - "uniform recovery of fusion frame structured sparse signals"

Connected through hyperedge HE-8 (induced by source paper "representations of sound in deep learning of audio features from music"):
  with members:
    - "acoustic scene classification classifying environments from the sounds they produce"

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)