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: "predicting smt solver performance for software verification"
  Abstract: The approach Why3 takes to interfacing with a wide variety of interactive#R##N#and automatic theorem provers works well: it is designed to overcome#R##N#limitations on what can be proved by a system which relies on a single#R##N#tightly-integrated solver. In common with other systems, however, the degree#R##N#to which proof obligations (or “goals”) are proved depends as much on#R##N#the SMT solver as the properties of the goal itself. In this work, we present a#R##N#method to use syntactic analysis to characterise goals and predict the most#R##N#appropriate solver via machine-learning techniques.#R##N#Combining solvers in this way - a portfolio-solving approach - maximises#R##N#the number of goals which can be proved. The driver-based architecture of#R##N#Why3 presents a unique opportunity to use a portfolio of SMT solvers for#R##N#software verification. The intelligent scheduling of solvers minimises the#R##N#time it takes to prove these goals by avoiding solvers which return Timeout#R##N#and Unknown responses. We assess the suitability of a number of machinelearning#R##N#algorithms for this scheduling task.#R##N#The performance of our tool Where4 is evaluated on a dataset of proof#R##N#obligations. We compare Where4 to a range of SMT solvers and theoretical#R##N#scheduling strategies. We find that Where4 can out-perform individual#R##N#solvers by proving a greater number of goals in a shorter average time.#R##N#Furthermore, Where4 can integrate into a Why3 user’s normal workflow -#R##N#simplifying and automating the non-expert use of SMT solvers for software#R##N#verification.

Connected through hyperedge HE-1 (induced by source paper "astra version 1 0 evaluating translations from alloy to smt lib"):
  with members:
    - "analyzing alloy formulas using an smt solver a case study"

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.SE(Software Engineering)