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: "chunky and equal spaced polynomial multiplication"
  Abstract: Finding the product of two polynomials is an essential and basic problem in computer algebra. While most previous results have focused on the worst-case complexity, we instead employ the technique of adaptive analysis to give an improvement in many "easy" cases. We present two adaptive measures and methods for polynomial multiplication, and also show how to effectively combine them to gain both advantages. One useful feature of these algorithms is that they essentially provide a gradient between existing "sparse" and "dense" methods. We prove that these approaches provide significant improvements in many cases but in the worst case are still comparable to the fastest existing algorithms.

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"
    - "output sensitive algorithms for sumset and sparse polynomial multiplication"
    - "multivariate sparse interpolation using randomized kronecker substitutions"

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"
    - "output sensitive algorithms for sumset and sparse polynomial multiplication"
    - "for all sparse recovery in near optimal time"
    - "nearly optimal sparse fourier transform"
    - "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"
    - "output sensitive algorithms for sumset and sparse polynomial multiplication"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "output sensitive algorithms for sumset and sparse polynomial multiplication"):
  with members:
    - "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"
    - "even faster integer multiplication"
    - "highly scalable multiplication for distributed sparse multivariate polynomials on many core systems"
    - "interpolation of shifted lacunary polynomials"

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)