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: "improving the use of equational constraints in cylindrical algebraic decomposition"
  Abstract: When building a cylindrical algebraic decomposition (CAD) savings can be made in the presence of an equational constraint (EC): an equation logically implied by a formula.   The present paper is concerned with how to use multiple ECs, propagating those in the input throughout the projection set. We improve on the approach of McCallum in ISSAC 2001 by using the reduced projection theory to make savings in the lifting phase (both to the polynomials we lift with and the cells lifted over). We demonstrate the benefits with worked examples and a complexity analysis.

Connected through hyperedge HE-1 (induced by source paper "deciding the consistency of non linear real arithmetic constraints with a conflict driven search using cylindrical algebraic coverings"):
  with members:
    - "cylindrical algebraic decomposition with equational constraints"
    - "using machine learning to improve cylindrical algebraic decomposition"
    - "algorithmically generating new algebraic features of polynomial systems for machine learning"
    - "improved cross validation for classifiers that make algorithmic choices to minimise runtime without compromising output correctness"
    - "comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition"
    - "theoryguru a mathematica package to apply quantifier elimination technology to economics"
    - "a case study on the parametric occurrence of multiple steady states"
    - "truth table invariant cylindrical algebraic decomposition"

Connected through hyperedge HE-2 (induced by source paper "cylindrical algebraic decomposition with equational constraints"):
  with members:
    - "identifying the parametric occurrence of multiple steady states for some biological networks"
    - "using machine learning to improve cylindrical algebraic decomposition"
    - "theoryguru a mathematica package to apply quantifier elimination technology to economics"
    - "non linear real arithmetic benchmarks derived from automated reasoning in economics"
    - "machine learning for mathematical software"
    - "towards incremental cylindrical algebraic decomposition in maple"
    - "symbolic versus numerical computation and visualization of parameter regions for multistationarity of biological networks"
    - "a case study on the parametric occurrence of multiple steady states"

Connected through hyperedge HE-3 (induced by source paper "using machine learning to improve cylindrical algebraic decomposition"):
  with members:
    - "machine learning for mathematical software"
    - "symbolic versus numerical computation and visualization of parameter regions for multistationarity of biological networks"
    - "a case study on the parametric occurrence of multiple steady states"
    - "need polynomial systems be doubly exponential"
    - "using machine learning to decide when to precondition cylindrical algebraic decomposition with groebner bases"
    - "truth table invariant cylindrical algebraic decomposition"
    - "the complexity of cylindrical algebraic decomposition with respect to polynomial degree"
    - "truth table invariant cylindrical algebraic decomposition by regular chains"

Connected through hyperedge HE-4 (induced by source paper "comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition"):
  with members:
    - "using machine learning to improve cylindrical algebraic decomposition"
    - "theoryguru a mathematica package to apply quantifier elimination technology to economics"
    - "non linear real arithmetic benchmarks derived from automated reasoning in economics"
    - "machine learning for mathematical software"
    - "symbolic versus numerical computation and visualization of parameter regions for multistationarity of biological networks"
    - "a case study on the parametric occurrence of multiple steady states"
    - "deepmath deep sequence models for premise selection"
    - "using machine learning to decide when to precondition cylindrical algebraic decomposition with groebner bases"

Connected through hyperedge HE-5 (induced by source paper "algorithmically generating new algebraic features of polynomial systems for machine learning"):
  with members:
    - "using machine learning to improve cylindrical algebraic decomposition"
    - "comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition"
    - "theoryguru a mathematica package to apply quantifier elimination technology to economics"
    - "non linear real arithmetic benchmarks derived from automated reasoning in economics"
    - "machine learning for mathematical software"
    - "symbolic versus numerical computation and visualization of parameter regions for multistationarity of biological networks"
    - "a case study on the parametric occurrence of multiple steady states"
    - "using machine learning to decide when to precondition cylindrical algebraic decomposition with groebner bases"

Connected through hyperedge HE-6 (induced by source paper "identifying the parametric occurrence of multiple steady states for some biological networks"):
  with members:
    - "using machine learning to improve cylindrical algebraic decomposition"
    - "symbolic versus numerical computation and visualization of parameter regions for multistationarity of biological networks"
    - "a case study on the parametric occurrence of multiple steady states"
    - "using machine learning to decide when to precondition cylindrical algebraic decomposition with groebner bases"
    - "truth table invariant cylindrical algebraic decomposition"
    - "the complexity of cylindrical algebraic decomposition with respect to polynomial degree"
    - "applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition"
    - "using the regular chains library to build cylindrical algebraic decompositions by projecting and lifting"

Connected through hyperedge HE-7 (induced by source paper "machine learning for mathematical software"):
  with members:
    - "a case study on the parametric occurrence of multiple steady states"
    - "improving sat solving with machine learning"
    - "using machine learning to decide when to precondition cylindrical algebraic decomposition with groebner bases"
    - "truth table invariant cylindrical algebraic decomposition"
    - "the complexity of cylindrical algebraic decomposition with respect to polynomial degree"
    - "applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition"
    - "pos tagging and its applications for mathematics"
    - "problem formulation for truth table invariant cylindrical algebraic decomposition by incremental triangular decomposition"

Connected through hyperedge HE-8 (induced by source paper "quantifier elimination for reasoning in economics"):
  with members:
    - "symbolic versus numerical computation and visualization of parameter regions for multistationarity of biological networks"
    - "a case study on the parametric occurrence of multiple steady states"
    - "using machine learning to decide when to precondition cylindrical algebraic decomposition with groebner bases"
    - "truth table invariant cylindrical algebraic decomposition"
    - "truth table invariant cylindrical algebraic decomposition by regular chains"
    - "applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition"
    - "optimising problem formulation for cylindrical algebraic decomposition"
    - "computing cylindrical algebraic decomposition via triangular decomposition"

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)