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: "an mdl framework for sparse coding and dictionary learning"
  Abstract: The power of sparse signal modeling with learned overcomplete dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical inference and machine learning. However, the statistical properties of these models, such as underfitting or overfitting given sets of data, are still not well characterized in the literature. As a result, the success of sparse modeling depends on hand-tuning critical parameters for each data and application. This work aims at addressing this by providing a practical and objective characterization of sparse models by means of the minimum description length (MDL) principle-a well-established information-theoretic approach to model selection in statistical inference. The resulting framework derives a family of efficient sparse coding and dictionary learning algorithms which, by virtue of the MDL principle, are completely parameter free. Furthermore, such framework allows to incorporate additional prior information to existing models, such as Markovian dependencies, or to define completely new problem formulations, including in the matrix analysis area, in a natural way. These virtues will be demonstrated with parameter-free algorithms for the classic image denoising and classification problems, and for low-rank matrix recovery in video applications. However, the framework is not limited to this imaging data, and can be applied to a wide range of signal and data types and 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 "statistical physics and information theory perspectives on linear inverse problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "phase transitions in sparse pca"
    - "compressive imaging via approximate message passing with image denoising"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "mismatched estimation in large linear systems"

Connected through hyperedge HE-3 (induced by source paper "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "can dmd obtain a scene background in color"
    - "efficient background modeling based on sparse representation and outlier iterative removal"
    - "fast algorithms for robust pca via gradient descent"
    - "fast robust pca on graphs"
    - "approximated robust principal component analysis for improved general scene background subtraction"
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"

Connected through hyperedge HE-4 (induced by source paper "a survey of sparse representation algorithms and applications"):
  with members:
    - "group based sparse representation for image restoration"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "constructing a non negative low rank and sparse graph with data adaptive features"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "compressed sensing signal recovery via forward backward pursuit"
    - "image super resolution via dual dictionary learning and sparse representation"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "fast l1 minimization algorithms for robust face recognition"

Connected through hyperedge HE-5 (induced by source paper "structured dictionary learning for classification"):
  with members:
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "learning transformations for clustering and classification"
    - "sparse subspace clustering algorithm theory and applications"
    - "exact recovery of sparsely used dictionaries"
    - "structured sparsity through convex optimization"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-6 (induced by source paper "multiple kernel sparse representations for supervised and unsupervised learning"):
  with members:
    - "learning stable multilevel dictionaries for sparse representations"

Connected through hyperedge HE-7 (induced by source paper "learning stable multilevel dictionaries for sparse representations"):
  with members:
    - "generalized principal component analysis gpca"

Connected through hyperedge HE-8 (induced by source paper "recovery from linear measurements with complexity matching universal signal estimation"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "empirical bayes and full bayes for signal estimation"
    - "compressed sensing via universal denoising and approximate message passing"
    - "performance regions in compressed sensing from noisy measurements"
    - "expectation maximization gaussian mixture approximate message passing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "an mcmc approach to universal lossy compression of analog sources"
    - "information complexity and estimation"

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.IT(Information Theory)