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: "what can and can t we do with sparse polynomials"
  Abstract: Simply put, a sparse polynomial is one whose zero coefficients are not explicitly stored. Such objects are ubiquitous in exact computing, and so naturally we would like to have efficient algorithms to handle them. However, with this compact storage comes new algorithmic challenges, as fast algorithms for dense polynomials may no longer be efficient. In this tutorial we examine the state of the art for sparse polynomial algorithms in three areas: arithmetic, interpolation, and factorization. The aim is to highlight recent progress both in theory and in practice, as well as opportunities for future work.

Connected through hyperedge HE-1 (induced by source paper "essentially optimal sparse polynomial multiplication"):
  with members:
    - "nearly optimal sparse polynomial multiplication"
    - "revisit sparse polynomial interpolation based on randomized kronecker substitution"
    - "output sensitive algorithms for sumset and sparse polynomial multiplication"
    - "multivariate sparse interpolation using randomized kronecker substitutions"
    - "chunky and equal spaced polynomial multiplication"

Connected through hyperedge HE-2 (induced by source paper "lu factorization with errors"):
  with members:
    - "error correction in fast matrix multiplication and inverse"
    - "certificates for triangular equivalence and rank profiles"
    - "linear time interactive certificates for the minimal polynomial and the determinant of a sparse matrix"
    - "efficiently correcting matrix products"
    - "essentially optimal interactive certificates in linear algebra"
    - "powers of tensors and fast matrix multiplication"
    - "parallel computation of echelon forms"
    - "simultaneous computation of the row and column rank profiles"

Connected through hyperedge HE-3 (induced by source paper "nearly optimal sparse polynomial multiplication"):
  with members:
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "output sensitive algorithms for sumset and sparse polynomial multiplication"
    - "for all sparse recovery in near optimal time"
    - "nearly optimal sparse fourier transform"
    - "chunky and equal spaced polynomial multiplication"
    - "approximate sparse recovery optimizing time and measurements"

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)