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: "complexity results and approximation strategies for map explanations"
  Abstract: MAP is the problem of finding a most probable instantiation of a set of variables given evidence. MAP has always been perceived to be significantly harder than the related problems of computing the probability of a variable instantiation (Pr), or the problem of computing the most probable explanation (MPE). This paper investigates the complexity of MAP in Bayesian networks. Specifically, we show that MAP is complete for NPPP and provide further negative complexity results for algorithms based on variable elimination. We also show that MAP remains hard even when MPE and Pr become easy. For example, we show that MAP is NP-complete when the networks are restricted to polytrees, and even then can not be effectively approximated.#R##N##R##N#Given the difficulty of computing MAP exactly, and the difficulty of approximating MAP while providing useful guarantees on the resulting approximation, we investigate best effort approximations. We introduce a generic MAP approximation framework. We provide two instantiations of the framework; one for networks which are amenable to exact inference (Pr), and one for networks for which even exact inference is too hard. This allows MAP approximation on networks that are too complex to even exactly solve the easier problems, Pr and MPE. Experimental results indicate that using these approximation algorithms provides much better solutions than standard techniques, and provide accurate MAP estimates in many cases.

Connected through hyperedge HE-1 (induced by source paper "approximate mmap by marginal search"):
  with members:
    - "solving map exactly using systematic search"

Connected through hyperedge HE-2 (induced by source paper "probabilistic parameterized polynomial time"):
  with members:
    - "and compression of np complete problems streamlined proof and minor observations"
    - "parameterized analogues of probabilistic computation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "on the latent variable interpretation in sum product networks"):
  with members:
    - "the sum product theorem a foundation for learning tractable models"
    - "a unified approach for learning the parameters of sum product networks"
    - "on the relationship between sum product networks and bayesian networks"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "a differential approach to inference in bayesian networks"
    - "context specific independence in bayesian networks"
    - "learning arithmetic circuits"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-5 (induced by source paper "message passing and combinatorial optimization"):
  with members:
    - "augmentative message passing for traveling salesman problem and graph partitioning"
    - "perturbed message passing for constraint satisfaction problems"
    - "revisiting algebra and complexity of inference in graphical models"
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "graphical models for game theory"
    - "message passing algorithms reparameterizations and splittings"
    - "the bethe permanent of a nonnegative matrix"

Connected through hyperedge HE-6 (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"
    - "solving map exactly using systematic search"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-7 (induced by source paper "constrained sampling and counting universal hashing meets sat solving"):
  with members:
    - "distribution aware sampling and weighted model counting for sat"
    - "balancing scalability and uniformity in sat witness generator"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "uniform solution sampling using a constraint solver as an oracle"
    - "the good old davis putnam procedure helps counting models"

Connected through hyperedge HE-8 (induced by source paper "revisiting algebra and complexity of inference in graphical models"):
  with members:
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "hybrid processing of beliefs and constraints"
    - "survey propagation revisited"
    - "robust max product belief propagation"
    - "approximating the permanent with belief propagation"
    - "an algebraic graphical model for decision with uncertainties feasibilities and utilities"
    - "a new look at survey propagation and its generalizations"

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)