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: "maximum likelihood bounded tree width markov networks"
  Abstract: Chow and Liu (1968) studied the problem of learning a maximumlikelihood Markov tree. We generalize their work to more complexMarkov networks by considering the problem of learning a maximumlikelihood Markov network of bounded complexity. We discuss howtree-width is in many ways the appropriate measure of complexity andthus analyze the problem of learning a maximum likelihood Markovnetwork of bounded tree-width.Similar to the work of Chow and Liu, we are able to formalize thelearning problem as a combinatorial optimization problem on graphs. Weshow that learning a maximum likelihood Markov network of boundedtree-width is equivalent to finding a maximum weight hypertree. Thisequivalence gives rise to global, integer-programming based,approximation algorithms with provable performance guarantees, for thelearning problem. This contrasts with heuristic local-searchalgorithms which were previously suggested (e.g. by Malvestuto 1991).The equivalence also allows us to study the computational hardness ofthe learning problem. We show that learning a maximum likelihoodMarkov network of bounded tree-width is NP-hard, and discuss thehardness of approximation.

Connected through hyperedge HE-1 (induced by source paper "efficient learning of optimal markov network topology with k tree modeling"):
  with members:
    - "ordering based search a simple and effective algorithm for learning bayesian networks"
    - "pac learning bounded tree width graphical models"
    - "multivariate information measures an experimentalist s perspective"

Connected through hyperedge HE-2 (induced by source paper "structure learning using forced pruning"):
  with members:
    - "beyond log supermodularity lower bounds and the bethe partition function"
    - "efficient stepwise selection in decomposable models"

Connected through hyperedge HE-3 (induced by source paper "structure learning of h colorings"):
  with members:
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "approximately counting h colourings is bis hard"
    - "hardness of parameter estimation in graphical models"
    - "correlation decay up to uniqueness in spin systems"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-4 (induced by source paper "information theoretic properties of markov random fields and their algorithmic applications"):
  with members:
    - "computational transition at the uniqueness threshold"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-5 (induced by source paper "efficiently learning ising models on arbitrary graphs"):
  with members:
    - "hardness of parameter estimation in graphical models"
    - "analysis of boolean functions"
    - "moment matching polynomials"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "on learning discrete graphical models using greedy methods"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "computational transition at the uniqueness threshold"
    - "polynomial learning of distribution families"

Connected through hyperedge HE-6 (induced by source paper "advances in learning bayesian networks of bounded treewidth"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "the bayesian structural em algorithm"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "advances in bayesian network learning using integer programming"
    - "bayesian network learning with cutting planes"
    - "exact structure discovery in bayesian networks with less space"
    - "a simple approach for finding the globally optimal bayesian network structure"

Connected through hyperedge HE-7 (induced by source paper "learning graphical models from the glauber dynamics"):
  with members:
    - "hardness of parameter estimation in graphical models"
    - "on learning discrete graphical models using greedy methods"
    - "uncovering the temporal dynamics of diffusion networks"
    - "topology discovery of sparse random graphs with few participants"
    - "on the convexity of latent social network inference"
    - "information theoretic limits of selecting binary graphical models in high dimensions"
    - "reconstruction of markov random fields from samples some easy observations and algorithms"

Connected through hyperedge HE-8 (induced by source paper "maximizing submodular functions using probabilistic graphical models"):
  with members:
    - "fast semidifferential based submodular function optimization"
    - "convex relaxations for learning bounded treewidth decomposable graphs"
    - "a submodular supermodular procedure with applications to discriminative structure learning"
    - "a new class of upper bounds on the log partition function"
    - "pac learning bounded tree width graphical models"
    - "near optimal nonmyopic value of information in graphical models"
    - "learning with submodular functions a convex optimization perspective"

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.LG(Machine Learning)