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: "a bayesian approach to tackling hard computational problems"
  Abstract: We are developing a general framework for using learned Bayesian models for decision-theoretic control of search and reasoningalgorithms. We illustrate the approach on the specific task of controlling both general and domain-specific solvers on a hard class of structured constraint satisfaction problems. A successful strategyfor reducing the high (and even infinite) variance in running time typically exhibited by backtracking search algorithms is to cut off and restart the search if a solution is not found within a certainamount of time. Previous work on restart strategies have employed fixed cut off values. We show how to create a dynamic cut off strategy by learning a Bayesian model that predicts the ultimate length of a trial based on observing the early behavior of the search algorithm. Furthermore, we describe the general conditions under which a dynamic restart strategy can outperform the theoretically optimal fixed strategy.

Connected through hyperedge HE-1 (induced by source paper "how much data is sufficient to learn high performing algorithms"):
  with members:
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a general theory of sample complexity for multi item profit maximization"
    - "efficiency and budget balance in general quasi linear domains"
    - "a pac approach to application specific algorithm selection"
    - "roberts theorem with neutrality a social welfare ordering approach"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-2 (induced by source paper "ml fv heartsuit a survey on the application of machine learning to formal verification"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deep network guided proof search"
    - "deepmath deep sequence models for premise selection"
    - "end to end learning for self driving cars"
    - "android malware detection an eigenspace analysis approach"
    - "machine learner for automated reasoning 0 4 and 0 5"

Connected through hyperedge HE-3 (induced by source paper "the algorithm selection competitions 2015 and 2017"):
  with members:
    - "aslib a benchmark library for algorithm selection"
    - "openml networked science in machine learning"
    - "a multi engine approach to answer set programming"
    - "sunny a lazy portfolio approach for constraint solving"
    - "claspfolio 2 advances in algorithm selection for answer set programming"
    - "proteus a hierarchical portfolio of solvers and transformations"
    - "llama leveraging learning to automatically manage algorithms"
    - "algorithm runtime prediction methods evaluation"

Connected through hyperedge HE-4 (induced by source paper "towards accountable ai hybrid human machine analyses for characterizing system failure"):
  with members:
    - "manipulating and measuring model interpretability"
    - "interpretable explorable approximations of black box models"
    - "an analysis of iso 26262 using machine learning safely in automotive software"
    - "on human intellect and machine failures troubleshooting integrative machine learning systems"
    - "spice semantic propositional image caption evaluation"
    - "concrete problems in ai safety"
    - "why should i trust you explaining the predictions of any classifier"
    - "cider consensus based image description evaluation"

Connected through hyperedge HE-5 (induced by source paper "the algorithm selection competition series 2015 17"):
  with members:
    - "aslib a benchmark library for algorithm selection"
    - "openml networked science in machine learning"
    - "sunny a lazy portfolio approach for constraint solving"
    - "claspfolio 2 advances in algorithm selection for answer set programming"
    - "proteus a hierarchical portfolio of solvers and transformations"
    - "llama leveraging learning to automatically manage algorithms"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

Connected through hyperedge HE-6 (induced by source paper "a pac approach to application specific algorithm selection"):
  with members:
    - "the pseudo dimension of near optimal auctions"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "algorithm runtime prediction methods evaluation"
    - "self improving algorithms for coordinate wise maxima"
    - "satzilla portfolio based algorithm selection for sat"
    - "truth revelation in approximately efficient combinatorial auctions"

Connected through hyperedge HE-7 (induced by source paper "metareasoning for planning under uncertainty"):
  with members:
    - "selecting computations theory and applications"
    - "reasoning metareasoning and mathematical truth studies of theorem proving under limited resources"
    - "ideal reformulation of belief networks"
    - "reasoning about beliefs and actions under computational resource constraints"

Connected through hyperedge HE-8 (induced by source paper "algorithm selection for combinatorial search problems a survey"):
  with members:
    - "sunny a lazy portfolio approach for constraint solving"
    - "claspfolio 2 advances in algorithm selection for answer set programming"
    - "proteus a hierarchical portfolio of solvers and transformations"
    - "algorithm portfolio design theory vs practice"
    - "new techniques for algorithm portfolio design"
    - "rational deployment of csp heuristics"
    - "machine learning for constraint solver design a case study for the alldifferent constraint"
    - "restart strategy selection using machine learning techniques"

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)