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 error of estimating the sparsest solution of underdetermined linear systems"
  Abstract: Let A be an n × m matrix with m >; n, and suppose that the underdetermined linear system As = x admits a sparse solution S0 for which ||S0||0 <; 1/2 spark( A). Such a sparse solution is unique due to a well-known uniqueness theorem. Suppose now that we have somehow a solution s as an estimation of s0, and suppose that s is only "approximately sparse," that is, many of its components are very small and nearly zero, but not mathematically equal to zero. Is such a solution necessarily close to the true sparsest solution? More generally, is it possible to construct an upper bound on the estimation error ||s - s0||2 without knowing S0? The answer is positive, and in this paper, we construct such a bound based on minimal singular values of submatrices of A. We will also state a tight bound, which is more complicated, but besides being tight, enables us to study the case of random dictionaries and obtain probabilistic upper bounds. We will also study the noisy case, that is, where x = As + n. Moreover, we will see that where ||s0 ||0 grows, to obtain a predetermined guaranty on the maximum of ||s - s0 ||2, s is needed to be sparse with a better approximation. This can be seen as an explanation to the fact that the estimation quality of sparse recovery algorithms degrades where ||s0||0 grows.

Connected through hyperedge HE-1 (induced by source paper "upper bounds on the error of sparse vector and low rank matrix recovery"):
  with members:
    - "successive concave sparsity approximation for compressed sensing"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "recovery of low rank matrices under affine constraints via a smoothed rank function"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

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)