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: "convex sparse matrix factorizations"
  Abstract: We present a convex formulation of dictionary learning for sparse signal decomposition. Convexity is obtained by replacing the usual explicit upper bound on the dictionary size by a convex rank-reducing term similar to the trace norm. In particular, our formulation introduces an explicit trade-off between size and sparsity of the decomposition of rectangular matrices. Using a large set of synthetic examples, we compare the estimation abilities of the convex and non-convex approaches, showing that while the convex formulation has a single local minimum, this may lead in some cases to performance which is inferior to the local minima of the non-convex formulation.

Connected through hyperedge HE-1 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "deep generalized canonical correlation analysis"
    - "strong data processing inequalities for channels and bayesian networks"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-2 (induced by source paper "online convex matrix factorization with representative regions"):
  with members:
    - "a ranking model motivated by nonnegative matrix factorization with applications to tennis tournaments"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "sparse approximation via generating point sets"
    - "simultaneous codeword optimization simco for dictionary update and learning"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "a stochastic proximal point algorithm for saddle point problems"):
  with members:
    - "saga with arbitrary sampling"
    - "local saddle point optimization a curvature exploitation approach"
    - "stochastic variance reduction methods for policy evaluation"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-4 (induced by source paper "recovery guarantees for quadratic tensors with limited observations"):
  with members:
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "exact tensor completion with sum of squares"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "recovery guarantee of non negative matrix factorization via alternating updates"
    - "riemannian preconditioning for tensor completion"
    - "efficient estimation of word representations in vector space"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-5 (induced by source paper "optimal sample complexity for matrix completion and related problems via 120001 s 2 regularization"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "how to escape saddle points efficiently"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "recovery guarantee of weighted low rank approximation via alternating minimization"
    - "a geometric analysis of phase retrieval"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"

Connected through hyperedge HE-6 (induced by source paper "an analysis of dropout for matrix factorization"):
  with members:
    - "dropout training of matrix factorization and autoencoder for link prediction in sparse graphs"
    - "global optimality in tensor factorization deep learning and beyond"
    - "on the inductive bias of dropout"
    - "convex relaxations of structured matrix factorizations"
    - "adding noise to the input of a model trained with a regularized objective"

Connected through hyperedge HE-7 (induced by source paper "structured low rank matrix factorization global optimality algorithms and applications"):
  with members:
    - "convex relaxations of structured matrix factorizations"

Connected through hyperedge HE-8 (induced by source paper "stochastic variance reduction methods for saddle point problems"):
  with members:
    - "stop wasting my gradients practical svrg"
    - "sketching as a tool for numerical linear algebra"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "optimization with sparsity inducing penalties"
    - "sublinear optimization for machine learning"

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)