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: "output sensitive algorithms for sumset and sparse polynomial multiplication"
  Abstract: We present randomized algorithms to compute the sumset (Minkowski sum) of two integer sets, and to multiply two univariate integer polynomials given by sparse representations. Our algorithm for sumset has cost softly linear in the combined size of the inputs and output. This is used as part of our sparse multiplication algorithm, whose cost is softly linear in the combined size of the inputs, output, and the sumset of the supports of the inputs. As a subroutine, we present a new method for computing the coefficients of a sparse polynomial, given a set containing its support. Our multiplication algorithm extends to multivariate Laurent polynomials over finite fields and rational numbers. Our techniques are based on sparse interpolation algorithms and results from analytic number theory.

Connected through hyperedge HE-1 (induced by source paper "essentially optimal sparse polynomial multiplication"):
  with members:
    - "nearly optimal sparse polynomial multiplication"
    - "what can and can t we do with sparse polynomials"
    - "revisit sparse polynomial interpolation based on randomized kronecker substitution"
    - "multivariate sparse interpolation using randomized kronecker substitutions"
    - "chunky and equal spaced polynomial multiplication"

Connected through hyperedge HE-2 (induced by source paper "nearly optimal sparse polynomial multiplication"):
  with members:
    - "what can and can t we do with sparse polynomials"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "what can and can t we do with sparse polynomials"):
  with members:
    - "bounded degree factors of lacunary multivariate polynomials"
    - "parallel sparse interpolation using small primes"
    - "sparse interpolation over finite fields via low order roots of unity"
    - "faster sparse multivariate polynomial interpolation of straight line programs"
    - "faster polynomial multiplication over finite fields"
    - "multivariate sparse interpolation using randomized kronecker substitutions"
    - "faster sparse interpolation of straight line programs"
    - "factoring bivariate lacunary polynomials without heights"

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)