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: "problem formulation for truth table invariant cylindrical algebraic decomposition by incremental triangular decomposition"
  Abstract: Cylindrical algebraic decompositions (CADs) are a key tool for solving problems in real algebraic geometry and beyond. We recently presented a new CAD algorithm combining two advances: truth-table invariance, making the CAD invariant with respect to the truth of logical formulae rather than the signs of polynomials; and CAD construction by regular chains technology, where first a complex decomposition is constructed by refining a tree incrementally by constraint. We here consider how best to formulate problems for input to this algorithm. We focus on a choice (not relevant for other CAD algorithms) about the order in which constraints are presented. We develop new heuristics to help make this choice and thus allow the best use of the algorithm in practice. We also consider other choices of problem formulation for CAD, as discussed in CICM 2013, revisiting these in the context of the new algorithm.

Connected through hyperedge HE-1 (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-2 (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"
    - "improving the use of equational constraints in cylindrical algebraic decomposition"

Connected through hyperedge HE-3 (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-4 (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"
    - "improving the use of equational constraints in cylindrical algebraic decomposition"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "truth table invariant cylindrical algebraic decomposition"):
  with members:
    - "improving the use of equational constraints in 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"
    - "choosing a variable ordering for truth table invariant cylindrical algebraic decomposition by incremental triangular decomposition"
    - "cylindrical algebraic decomposition using local projections"
    - "using the distribution of cells by dimension in a cylindrical algebraic decomposition"
    - "using the regular chains library to build cylindrical algebraic decompositions by projecting and lifting"
    - "cylindrical algebraic sub decompositions"

Connected through hyperedge HE-6 (induced by source paper "experience with heuristics benchmarks standards for cylindrical algebraic decomposition"):
  with members:
    - "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"
    - "improving the use of equational constraints in cylindrical algebraic decomposition"
    - "a comparison of three heuristics to choose the variable ordering for 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"

Connected through hyperedge HE-7 (induced by source paper "the complexity of cylindrical algebraic decomposition with respect to polynomial degree"):
  with members:
    - "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"
    - "improving the use of equational constraints in 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"
    - "cylindrical algebraic decomposition using local projections"
    - "using the distribution of cells by dimension in a cylindrical algebraic decomposition"

Connected through hyperedge HE-8 (induced by source paper "need polynomial systems be doubly exponential"):
  with members:
    - "truth table invariant cylindrical algebraic decomposition"
    - "the complexity of cylindrical algebraic decomposition with respect to polynomial degree"
    - "improving the use of equational constraints in 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"
    - "cylindrical algebraic decomposition using local projections"
    - "using the distribution of cells by dimension in a cylindrical algebraic decomposition"
    - "cylindrical algebraic sub decompositions"

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)