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: "the configurable sat solver challenge cssc"
  Abstract: It is well known that different solution strategies work well for different types of instances of hard combinatorial problems. As a consequence, most solvers for the propositional satisfiability problem (SAT) expose parameters that allow them to be customized to a particular family of instances. In the international SAT competition series, these parameters are ignored: solvers are run using a single default parameter setting (supplied by the authors) for all benchmark instances in a given track. While this competition format rewards solvers with robust default settings, it does not reflect the situation faced by a practitioner who only cares about performance on one particular application and can invest some time into tuning solver parameters for this application. The new Configurable SAT Solver Competition (CSSC) compares solvers in this latter setting, scoring each solver by the performance it achieved after a fully automated configuration step. This article describes the CSSC in more detail, and reports the results obtained in its two instantiations so far, CSSC 2013 and 2014.

Connected through hyperedge HE-1 (induced by source paper "improving the performance of stochastic local search for maximum vertex weight clique problem using programming by optimization"):
  with members:
    - "dynamic local search for the maximum clique problem"
    - "a simple model to generate hard satisfiable instances"

Connected through hyperedge HE-2 (induced by source paper "towards white box benchmarks for algorithm control"):
  with members:
    - "theory of parameter control for discrete black box optimization provable performance gains through dynamic parameter choices"
    - "rllib abstractions for distributed reinforcement learning"
    - "learning to learn by gradient descent by gradient descent"
    - "sgdr stochastic gradient descent with restarts"
    - "learning to optimize"
    - "asynchronous methods for deep reinforcement learning"
    - "layer specific adaptive learning rates for deep networks"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "on performance estimation in automatic algorithm configuration"):
  with members:
    - "automatic construction of parallel portfolios via explicit instance grouping"
    - "leapsandbounds a method for approximately optimal algorithm configuration"

Connected through hyperedge HE-4 (induced by source paper "pitfalls and best practices in algorithm configuration"):
  with members:
    - "deep reinforcement learning that matters"
    - "algorithm selection for combinatorial search problems a survey"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "satzilla portfolio based algorithm selection for sat"
    - "a critical assessment of benchmark comparison in planning"

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.AI(Artificial Intelligence)