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: "nearly optimal computations with structured matrices"
  Abstract: We estimate the Boolean complexity of multiplication of structured matrices by a vector and the solution of nonsingular linear systems of equations with these matrices. We study four basic most popular classes, that is, Toeplitz, Hankel, Cauchy and Van-der-monde matrices, for which the cited computational problems are equivalent to the task of polynomial multiplication and division and polynomial and rational multipoint evaluation and interpolation. The Boolean cost estimates for the latter problems have been obtained by Kirrinnis in \cite{kirrinnis-joc-1998}, except for rational interpolation, which we supply now. All known Boolean cost estimates for these problems rely on using Kronecker product. This implies the $d$-fold precision increase for the $d$-th degree output, but we avoid such an increase by relying on distinct techniques based on employing FFT. Furthermore we simplify the analysis and make it more transparent by combining the representation of our tasks and algorithms in terms of both structured matrices and polynomials and rational functions. This also enables further extensions of our estimates to cover Trummer's important problem and computations with the popular classes of structured matrices that generalize the four cited basic matrix classes.

Connected through hyperedge HE-1 (induced by source paper "new progress in univariate polynomial root finding"):
  with members:
    - "polynomial root clustering and explicit deflation"
    - "implementation of a near optimal complex root clustering algorithm"
    - "computing real roots of real polynomials and now for real"
    - "real polynomial root finding by means of matrix and polynomial iterations"
    - "a near optimal subdivision algorithm for complex root isolation based on the pellet test and newton iteration"
    - "a numerical algorithm for zero counting i complexity and accuracy"

Connected through hyperedge HE-2 (induced by source paper "a nearly optimal algorithm to decompose binary forms"):
  with members:
    - "reconstruction algorithms for sums of affine powers"
    - "symmetric tensor decomposition"

Connected through hyperedge HE-3 (induced by source paper "fast feasible and unfeasible matrix multiplication"):
  with members:
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "an improved combinatorial algorithm for boolean matrix multiplication"
    - "improving the numerical stability of fast matrix multiplication algorithms"
    - "a framework for practical parallel fast matrix multiplication"
    - "fast matrix multiplication limitations of the laser method"
    - "powers of tensors and fast matrix multiplication"
    - "better late than never filling a void in the history of fast matrix multiplication and tensor decompositions"

Connected through hyperedge HE-4 (induced by source paper "polynomial factorization is simple and helpful more so than it seems to be"):
  with members:
    - "polynomial root clustering and explicit deflation"
    - "implementation of a near optimal complex root clustering algorithm"
    - "computing real roots of real polynomials and now for real"
    - "a near optimal subdivision algorithm for complex root isolation based on the pellet test and newton iteration"

Connected through hyperedge HE-5 (induced by source paper "on matrices with displacement structure generalized operators and faster algorithms"):
  with members:
    - "faster algorithms for multivariate interpolation with multiplicities and simultaneous polynomial approximations"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-6 (induced by source paper "a two pronged progress in structured dense matrix multiplication"):
  with members:
    - "note on fast division algorithm for polynomials using newton iteration"
    - "fast conversion algorithms for orthogonal polynomials"

Connected through hyperedge HE-7 (induced by source paper "faq questions asked frequently"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "computing join queries with functional dependencies"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "join processing for graph patterns an old dog with new tricks"
    - "emptyheaded boolean algebra based graph processing"
    - "joins via geometric resolutions worst case and beyond"

Connected through hyperedge HE-8 (induced by source paper "accelerated approximation of the complex roots of a univariate polynomial extended abstract"):
  with members:
    - "fast approximate polynomial multipoint evaluation and applications"
    - "from approximate factorization to root isolation with application to cylindrical algebraic decomposition"

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.SC(Symbolic Computation)