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 "using machine learning to decide when to precondition cylindrical algebraic decomposition with groebner bases"):
  Abstract: Cylindrical Algebraic Decomposition (CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. However, it can be expensive, with worst case complexity doubly exponential in the size of the input. Hence it is important to formulate the problem in the best manner for the CAD algorithm. One possibility is to precondition the input polynomials using Groebner Basis (GB) theory. Previous experiments have shown that while this can often be very beneficial to the CAD algorithm, for some problems it can significantly worsen the CAD performance. In the present paper we investigate whether machine learning, specifically a support vector machine (SVM), may be used to identify those CAD problems which benefit from GB preconditioning. We run experiments with over 1000 problems (many times larger than previous studies) and find that the machine learned choice does better than the human-made heuristic.

Includes members:
  Member "need polynomial systems be doubly exponential":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using machine learning to improve cylindrical algebraic decomposition")
  Member "truth table invariant cylindrical algebraic decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deciding the consistency of non linear real arithmetic constraints with a conflict driven search using cylindrical algebraic coverings")
  Member "the complexity of cylindrical algebraic decomposition with respect to polynomial degree":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deciding the consistency of non linear real arithmetic constraints with a conflict driven search using cylindrical algebraic coverings")
  Member "improving the use of equational constraints in cylindrical algebraic decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deciding the consistency of non linear real arithmetic constraints with a conflict driven search using cylindrical algebraic coverings")
  Member "truth table invariant cylindrical algebraic decomposition by regular chains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cylindrical algebraic decomposition with equational constraints")
  Member "applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cylindrical algebraic decomposition with equational constraints")
  Member "choosing a variable ordering for truth table invariant cylindrical algebraic decomposition by incremental triangular decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using machine learning to improve cylindrical algebraic decomposition")
  Member "cylindrical algebraic decomposition using local projections":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cylindrical algebraic decomposition with equational constraints")

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)