human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "nearly optimal sparse polynomial multiplication"):
  Abstract: In the sparse multiplication problem, one is asked to multiply two sparse polynomials $f$ and $g$ in time that is proportional to the size of the input plus the size of the output. The polynomials are given via lists of their coefficients $F$ and $G$, respectively. Cole and Hariharan (STOC 02) have given a nearly optimal algorithm when the coefficients are positive, and Arnold and Roche (ISSAC 15) devised an algorithm running in time proportional to the ``structural sparsity'' of the product, i.e. the set $\mathrm{supp}(F) + \mathrm{supp}(G)$. The latter algorithm is particularly efficient when there not ''too many cancellations'' of coefficients in the product. #R##N#In this work we give a clean, nearly optimal algorithm for the sparse polynomial multiplication problem, resolving an open question posed by Roche (ISSAC 18).

Includes members:
  Member "what can and can t we do with sparse polynomials":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "essentially optimal sparse polynomial multiplication")
  Member "sample efficient estimation and recovery in sparse fft via isolation on average":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "output sensitive algorithms for sumset and sparse polynomial multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "essentially optimal sparse polynomial multiplication")
  Member "for all sparse recovery in near optimal time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "nearly optimal sparse fourier transform":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "chunky and equal spaced polynomial multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "essentially optimal sparse polynomial multiplication")
  Member "approximate sparse recovery optimizing time and measurements":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "privacy preserving learning via deep net pruning")

Question: Please tell me which class the source paper 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)