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: "matrix sparsification and the sparse null space problem"
  Abstract: We revisit the matrix problems sparse null space and matrix sparsification, and show that they are equivalent. We then proceed to seek algorithms for these problems: We prove the hardness of approximation of these problems, and also give a powerful tool to extend algorithms and heuristics for sparse approximation theory to these problems.

Connected through hyperedge HE-1 (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-2 (induced by source paper "finding a sparse vector in a subspace linear sparsity using alternating directions"):
  with members:
    - "a geometric analysis of phase retrieval"
    - "simple efficient and neural algorithms for sparse coding"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "tensor vs matrix methods robust tensor decomposition under block sparse perturbations"
    - "sum of squares lower bounds for sparse pca"
    - "speeding up sum of squares for tensor decomposition and planted sparse vectors"
    - "finding a low rank basis in a matrix subspace"
    - "dual principal component pursuit"

Connected through hyperedge HE-3 (induced by source paper "complete dictionary recovery over the sphere"):
  with members:
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "guaranteed matrix completion via non convex factorization"
    - "simple efficient and neural algorithms for sparse coding"
    - "convergence radius and sample complexity of itkm algorithms for dictionary learning"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "on the computational efficiency of training neural networks"

Connected through hyperedge HE-4 (induced by source paper "on the computational intractability of exact and approximate dictionary learning"):
  with members:
    - "more algorithms for provable dictionary learning"
    - "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-5 (induced by source paper "rounding sum of squares relaxations"):
  with members:
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "towards a better approximation for sparsest cut"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "hidden cliques and the certification of the restricted isometry property"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximability and proof complexity"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-6 (induced by source paper "exact recovery of sparsely used dictionaries"):
  with members:
    - "convex sparse matrix factorizations"

Connected through hyperedge HE-7 (induced by source paper "efficient joint network source coding for multiple terminals with side information"):
  with members:
    - "algebraic codes for slepian wolf code design"
    - "network information flow with correlated sources"

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.CC(Computational Complexity)