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: "restart strategy selection using machine learning techniques"
  Abstract: Restart strategies are an important factor in the performance of conflict-driven Davis Putnam style SAT solvers. Selecting a good restart strategy for a problem instance can enhance the performance of a solver. Inspired by recent success applying machine learning techniques to predict the runtime of SAT solvers, we present a method which uses machine learning to boost solver performance through a smart selection of the restart strategy. Based on easy to compute features, we train both a satisfiability classifier and runtime models. We use these models to choose between restart strategies. We present experimental results comparing this technique with the most commonly used restart strategies. Our results demonstrate that machine learning is effective in improving solver performance.

Connected through hyperedge HE-1 (induced by source paper "extreme algorithm selection with dyadic feature representation"):
  with members:
    - "automated algorithm selection survey and perspectives"
    - "aslib a benchmark library for algorithm selection"
    - "openml networked science in machine learning"
    - "a theoretical analysis of ndcg type ranking measures"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-2 (induced by source paper "pdp a general neural framework for learning constraint satisfaction solvers"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "deep sets"
    - "streamlining variational inference for constraint satisfaction problems"
    - "inference in probabilistic graphical models by graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "improving sat solver heuristics with graph networks and reinforcement learning"):
  with members:
    - "reinforcement learning driven heuristic optimization"
    - "guiding high performance sat solvers with unsat core predictions"
    - "learning transferable cooperative behavior in multi agent teams"
    - "graph representations for higher order logic and theorem proving"
    - "fast graph representation learning with pytorch geometric"
    - "structured agents for physical construction"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "learning a sat solver from single bit supervision"

Connected through hyperedge HE-4 (induced by source paper "the potential of restarts for probsat"):
  with members:
    - "runtime distributions and criteria for restarts"
    - "predicting runtime distributions using deep neural networks"
    - "evaluating las vegas algorithms pitfalls and remedies"

Connected through hyperedge HE-5 (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-6 (induced by source paper "learning a sat solver from single bit supervision"):
  with members:
    - "recurrent relational networks for complex relational reasoning"
    - "can neural networks understand logical entailment"
    - "recurrent relational networks"
    - "neural message passing for quantum chemistry"
    - "learning combinatorial optimization algorithms over graphs"
    - "gated graph sequence neural networks"
    - "neural combinatorial optimization with reinforcement learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "predicting runtime distributions using deep neural networks"):
  with members:
    - "claspfolio 2 advances in algorithm selection for answer set programming"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-8 (induced by source paper "proteus a hierarchical portfolio of solvers and transformations"):
  with members:
    - "symbiosis of search and heuristics for random 3 sat"
    - "llama leveraging learning to automatically manage algorithms"
    - "algorithm selection for combinatorial search problems a survey"
    - "machine learning for constraint solver design a case study for the alldifferent constraint"
    - "xml representation of constraint networks format xcsp 2 1"
    - "satzilla portfolio based algorithm selection for sat"

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)