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: "solving map exactly using systematic search"
  Abstract: MAP is the problem of finding a most probable instantiation of a set of variables in a Bayesian network given some evidence. Unlike computing posterior probabilities, or MPE (a special case of MAP), the time and space complexity of structural solutions for MAP are not only exponential in the network treewidth, but in a larger parameter known as the "constrained" treewidth. In practice, this means that computing MAP can be orders of magnitude more expensive than computing posterior probabilities or MPE. This paper introduces a new, simple upper bound on the probability of a MAP solution, which admits a tradeoff between the bound quality and the time needed to compute it. The bound is shown to be generally much tighter than those of other methods of comparable complexity. We use this proposed upper bound to develop a branch-and-bound search algorithm for solving MAP exactly. Experimental results demonstrate that the search algorithm is able to solve many problems that are far beyond the reach of any structure-based method for MAP. For example, we show that the proposed algorithm can compute MAP exactly and efficiently for some networks whose constrained treewidth is more than 40.

Connected through hyperedge HE-1 (induced by source paper "approximate mmap by marginal search"):
  with members:
    - "complexity results and approximation strategies for map explanations"

Connected through hyperedge HE-2 (induced by source paper "decomposition bounds for marginal map"):
  with members:
    - "message passing algorithms reparameterizations and splittings"
    - "convergent message passing algorithms for inference over general graphs with convex free energies"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "convergent message passing algorithms a unifying view"
    - "tightening lp relaxations for map using message passing"
    - "join graph based cost shifting schemes"
    - "a new class of upper bounds on the log partition function"
    - "tightening fractional covering upper bounds on the partition function for high order region graphs"

Connected through hyperedge HE-3 (induced by source paper "maximum a posteriori estimation by search in probabilistic programs"):
  with members:
    - "selecting computations theory and applications"
    - "church a language for generative models"
    - "anytime marginal map inference"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-4 (induced by source paper "exact decoding on latent variable conditional models is np hard"):
  with members:
    - "complexity results and approximation strategies for map explanations"

Connected through hyperedge HE-5 (induced by source paper "exploiting evidence in probabilistic inference"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "a differential approach to inference in bayesian networks"
    - "context specific independence in bayesian networks"
    - "evidence absorption and propagation through evidence reversals"
    - "sensitivity analysis in bayesian networks from single to multiple parameters"

Connected through hyperedge HE-6 (induced by source paper "dynamic stochastic orienteering problems for risk aware applications"):
  with members:
    - "robust local search for solving rcpsp max with durational uncertainty"

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

Connected through hyperedge HE-8 (induced by source paper "new results for the map problem in bayesian networks"):
  with members:
    - "binary join trees"
    - "complexity results and approximation strategies for map explanations"
    - "the computational complexity of probabilistic 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)