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: "on the computational intractability of exact and approximate dictionary learning"
  Abstract: The efficient sparse coding and reconstruction of signal vectors via linear observations has received a tremendous amount of attention over the last decade. In this context, the automated learning of a suitable basis or overcomplete dictionary from training data sets of certain signal classes for use in sparse representations has turned out to be of particular importance regarding practical signal processing applications. Most popular dictionary learning algorithms involve NP-hard sparse recovery problems in each iteration, which may give some indication about the complexity of dictionary learning but does not constitute an actual proof of computational intractability. In this technical note, we show that learning a dictionary with which a given set of training signals can be represented as sparsely as possible is indeed \ssb NP-hard. Moreover, we also establish hardness of approximating the solution to within large factors of the optimal sparsity level. Furthermore, we give \ssb NP-hardness and non-approximability results for a recent dictionary learning variation called the sensor permutation problem. Along the way, we also obtain a new non-approximability result for the classical sparse recovery problem from compressed sensing.

Connected through hyperedge HE-1 (induced by source paper "approximate guarantees for dictionary learning"):
  with members:
    - "towards learning sparsely used dictionaries with arbitrary supports"
    - "sparse approximation via generating point sets"
    - "variable selection is hard"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "hypercontractivity sum of squares proofs and their applications"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-2 (induced by source paper "a tree based dictionary learning framework"):
  with members:
    - "a haar wavelet based perceptual similarity index for image quality assessment"
    - "a region based easy path wavelet transform for sparse image representation"
    - "on sparse representation in fourier and local bases"
    - "the haar wavelet transform of a dendrogram"

Connected through hyperedge HE-3 (induced by source paper "unlabelled sensing a sparse bayesian learning approach"):
  with members:
    - "signal recovery from unlabeled samples"
    - "signal amplitude estimation and detection from unlabeled binary quantized samples"
    - "unlabeled sensing with random linear measurements"

Connected through hyperedge HE-4 (induced by source paper "sparse range constrained learning and its application for medical image grading"):
  with members:
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "sparse coding and autoencoders"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "electron proton dynamics in deep learning"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "when is a convolutional filter easy to learn"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "convergence analysis of two layer neural networks with relu activation"
    - "an improved analysis of the er spud dictionary learning algorithm"

Connected through hyperedge HE-6 (induced by source paper "learning fast sparsifying transforms"):
  with members:
    - "fast orthonormal sparsifying transforms based on householder reflectors"
    - "trainlets dictionary learning in high dimensions"
    - "fast approximation of rotations and hessians matrices"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "fast orthonormal sparsifying transforms based on householder reflectors"):
  with members:
    - "more algorithms for provable dictionary learning"
    - "fast approximation of rotations and hessians matrices"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "learning computationally efficient dictionaries and their implementation as fast transforms"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "exact recovery of sparsely used dictionaries"
    - "dictionary learning for deblurring and digital zoom"

Connected through hyperedge HE-8 (induced by source paper "dictionary learning with equiprobable matching pursuit"):
  with members:
    - "towards a mathematical theory of super resolution"

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)