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: "sparse and spurious dictionary learning with noise and outliers"
  Abstract: A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictionary. While this paradigm has led to numerous empirical successes in various fields ranging from image to audio processing, there have only been a few theoretical arguments supporting these evidences. In particular, sparse coding, or sparse dictionary learning, relies on a non-convex procedure whose local minima have not been fully analyzed yet. In this paper, we consider a probabilistic model of sparse signals, and show that, with high probability, sparse coding admits a local minimum around the reference dictionary generating the signals. Our study takes into account the case of over-complete dictionaries, noisy signals, and possible outliers, thus extending previous work limited to noiseless settings and/or under-complete dictionaries. The analysis we conduct is non-asymptotic and makes it possible to understand how the key quantities of the problem, such as the coherence or the level of noise, can scale with respect to the dimension of the signals, the number of atoms, the sparsity and the number of observations.

Connected through hyperedge HE-1 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms"):
  with members:
    - "minimax lower bounds on dictionary learning for tensor data"
    - "tensorizing neural networks"
    - "denoising and completion of 3d data via multidimensional dictionary learning"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"

Connected through hyperedge HE-2 (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"
    - "on the computational intractability of exact and approximate dictionary learning"
    - "variable selection is hard"
    - "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-3 (induced by source paper "generalization error in deep learning"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "dnn or k nn that is the generalize vs memorize question"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "adversarially robust generalization requires more data"
    - "deep neural networks with multi branch architectures are less non convex"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-4 (induced by source paper "neural random projections for language modelling"):
  with members:
    - "attention is all you need"
    - "language modeling with gated convolutional networks"
    - "exploring the limits of language modeling"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "text understanding from scratch"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "minimax lower bounds on dictionary learning for tensor data"):
  with members:
    - "minimax lower bounds for kronecker structured dictionary learning"
    - "a tensor based dictionary learning approach to tomographic image reconstruction"
    - "denoising and completion of 3d data via multidimensional dictionary learning"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "local identification of overcomplete dictionaries"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"

Connected through hyperedge HE-6 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture"):
  with members:
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "a geometric analysis of phase retrieval"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "simple efficient and neural algorithms for sparse coding"

Connected through hyperedge HE-7 (induced by source paper "blind signal detection in massive mimo exploiting the channel sparsity"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "blind estimation of sparse multi user massive mimo channels"
    - "bilinear generalized approximate message passing part i derivation"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "blind pilot decontamination"
    - "large system analysis of linear precoding in miso broadcast channels with confidential messages"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"

Connected through hyperedge HE-8 (induced by source paper "dictionary learning from incomplete data"):
  with members:
    - "simple efficient and neural algorithms for sparse coding"
    - "convergence radius and sample complexity of itkm algorithms for dictionary learning"
    - "complete dictionary recovery over the sphere"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "local identification of overcomplete dictionaries"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

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)