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: "approximating map using local search"
  Abstract: MAP is the problem of finding a most probable instantiation of a set of variables in a Bayesian network, given evidence. Unlike computing marginals, posteriors, and MPE (a special case of MAP), the time and space complexity of MAP is not only exponential in the network treewidth, but also in a larger parameter known as the "constrained" treewidth. In practice, this means that computing MAP can be orders of magnitude more expensive than computingposteriors or MPE. Thus, practitioners generally avoid MAP computations, resorting instead to approximating them by the most likely value for each MAP variableseparately, or by MPE.We present a method for approximating MAP using local search. This method has space complexity which is exponential onlyin the treewidth, as is the complexity of each search step. We investigate the effectiveness of different local searchmethods and several initialization strategies and compare them to otherapproximation schemes.Experimental results show that local search provides a much more accurate approximation of MAP, while requiring few search steps.Practically, this means that the complexity of local search is often exponential only in treewidth as opposed to the constrained treewidth, making approximating MAP as efficient as other computations.

Connected through hyperedge HE-1 (induced by source paper "survey on models and techniques for root cause analysis"):
  with members:
    - "learning possibilistic logic theories from default rules"
    - "learning tractable probabilistic models for fault localization"
    - "using abduction in markov logic networks for root cause analysis"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "automated reasoning using possibilistic logic semantics belief revision and variable certainty weights"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "evaluating computational models of explanation using human judgments"

Connected through hyperedge HE-2 (induced by source paper "a differential approach to inference in bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "making sensitivity analysis computationally efficient"
    - "map complexity results and approximation methods"
    - "when do numbers really matter"
    - "a knowledge compilation map"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-3 (induced by source paper "phase transition of tractability in constraint satisfaction and bayesian network inference"):
  with members:
    - "optimal junction trees"

Connected through hyperedge HE-4 (induced by source paper "map complexity results and approximation methods"):
  with members:
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-5 (induced by source paper "solving map exactly using systematic search"):
  with members:
    - "map complexity results and approximation methods"

Connected through hyperedge HE-6 (induced by source paper "the minimum information principle for discriminative learning"):
  with members:
    - "iterative markov chain monte carlo computation of reference priors and minimax risk"

Connected through hyperedge HE-7 (induced by source paper "maximum error modeling for fault tolerant computation using maximum a posteriori map hypothesis"):
  with members:
    - "solving map exactly using systematic search"

Connected through hyperedge HE-8 (induced by source paper "solving factored mdps with hybrid state and action variables"):
  with members:
    - "from influence diagrams to junction trees"
    - "metrics for finite markov decision processes"
    - "dynamic programming for structured continuous markov decision problems"
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "approximate linear programming for first order mdps"
    - "solving map exactly using systematic search"
    - "metrics for markov decision processes with infinite state spaces"
    - "solving factored mdps with continuous and discrete variables"

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)