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 "improved cross validation for classifiers that make algorithmic choices to minimise runtime without compromising output correctness"):
  Abstract: Our topic is the use of machine learning to improve software by making choices which do not compromise the correctness of the output, but do affect the time taken to produce such output. We are particularly concerned with computer algebra systems (CASs), and in particular, our experiments are for selecting the variable ordering to use when performing a cylindrical algebraic decomposition of $n$-dimensional real space with respect to the signs of a set of polynomials. #R##N#In our prior work we explored the different ML models that could be used, and how to identify suitable features of the input polynomials. In the present paper we both repeat our prior experiments on problems which have more variables (and thus exponentially more possible orderings), and examine the metric which our ML classifiers targets. The natural metric is computational runtime, with classifiers trained to pick the ordering which minimises this. However, this leads to the situation were models do not distinguish between any of the non-optimal orderings, whose runtimes may still vary dramatically. In this paper we investigate a modification to the cross-validation algorithms of the classifiers so that they do distinguish these cases, leading to improved results.

Includes members:
  Member "identifying the parametric occurrence of multiple steady states for some biological networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cylindrical algebraic decomposition with equational constraints")
  Member "cylindrical algebraic decomposition with equational constraints":
    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 "using machine learning to improve cylindrical algebraic decomposition":
    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 "algorithmically generating new algebraic features of polynomial systems for machine learning":
    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 "comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deciding the consistency of non linear real arithmetic constraints with a conflict driven search using cylindrical algebraic coverings")
  Member "theoryguru a mathematica package to apply quantifier elimination technology to economics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deciding the consistency of non linear real arithmetic constraints with a conflict driven search using cylindrical algebraic coverings")
  Member "non linear real arithmetic benchmarks derived from automated reasoning in economics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cylindrical algebraic decomposition with equational constraints")
  Member "machine learning for mathematical software":
    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)