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: "map complexity results and approximation methods"
  Abstract: MAP is the problem of finding a most probable instantiation of a set of nvariables in a Bayesian network, given some evidence. MAP appears to be a significantly harder problem than the related problems of computing the probability of evidence Pr, or MPE a special case of MAP. Because of the complexity of MAP, and the lack of viable algorithms to approximate it,MAP computations are generally avoided by practitioners. This paper investigates the complexity of MAP. We show that MAP is complete for NP. We also provide negative complexity results for elimination based algorithms. It turns out that MAP remains hard even when MPE, and Pr are easy. We show that MAP is NPcomplete when the networks are restricted to polytrees, and even then can not be effectively approximated. Because there is no approximation algorithm with guaranteed results, we investigate best effort approximations. We introduce a generic MAP approximation framework. As one instantiation of it, we implement local search coupled with belief propagation BP to approximate MAP. We show how to extract approximate evidence retraction information from belief propagation which allows us to perform efficient local search. This allows MAP approximation even on networks that are too complex to even exactly solve the easier problems of computing Pr or MPE. Experimental results indicate that using BP and local search provides accurate MAP estimates in many cases.

Connected through hyperedge HE-1 (induced by source paper "a rate distortion framework for explaining neural network decisions"):
  with members:
    - "the computational complexity of understanding network decisions"
    - "lightweight probabilistic deep networks"
    - "innvestigate neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "smoothgrad removing noise by adding noise"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "the computational complexity of understanding network decisions"):
  with members:
    - "a rate distortion framework for explaining neural network decisions"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "deeppose human pose estimation via deep neural networks"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "speech recognition with deep recurrent neural networks"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-3 (induced by source paper "lifted marginal map inference"):
  with members:
    - "solving marginal map problems with np oracles and parity constraints"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "lifted graphical models a survey"

Connected through hyperedge HE-4 (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-5 (induced by source paper "maximum a posteriori inference in sum product networks"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "approximation complexity of maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "on the relationship between sum product networks and bayesian networks"
    - "a differential approach to inference in bayesian networks"
    - "learning arithmetic circuits"
    - "sum product networks a new deep architecture"
    - "a knowledge compilation map"

Connected through hyperedge HE-6 (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-7 (induced by source paper "markov logic networks for natural language question answering"):
  with members:
    - "probabilistic theorem proving"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"
    - "parameter learning of logic programs for symbolic statistical modeling"

Connected through hyperedge HE-8 (induced by source paper "distribution aware sampling and weighted model counting for sat"):
  with members:
    - "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"
    - "probabilistic planning via heuristic forward search and weighted model counting"
    - "complexity results and approximation strategies for map explanations"

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)