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: "comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition"
  Abstract: There has been recent interest in the use of machine learning (ML) approaches within mathematical software to make choices that impact on the computing performance without affecting the mathematical correctness of the result. We address the problem of selecting the variable ordering for cylindrical algebraic decomposition (CAD), an important algorithm in Symbolic Computation. Prior work to apply ML on this problem implemented a Support Vector Machine (SVM) to select between three existing human-made heuristics, which did better than anyone heuristic alone. Here we extend this result by training ML models to select the variable ordering directly, and by trying out a wider variety of ML techniques.

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"
    - "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"
    - "the complexity of cylindrical algebraic decomposition with respect to polynomial degree"

Connected through hyperedge HE-2 (induced by source paper "improved cross validation for classifiers that make algorithmic choices to minimise runtime without compromising output correctness"):
  with members:
    - "identifying the parametric occurrence of multiple steady states for some biological networks"
    - "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"
    - "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"
    - "a case study on the parametric occurrence of multiple steady states"

Connected through hyperedge HE-3 (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"
    - "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"
    - "truth table invariant cylindrical algebraic 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)