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: "pac learning bounded tree width graphical models"
  Abstract: We show that the class of strongly connected graphical models with treewidth at most k can be properly efficiently PAC-learnt with respect to the Kullback-Leibler Divergence. Previous approaches to this problem, such as those of Chow ([1]), and Ho gen ([7]) have shown that this class is PAC-learnable by reducing it to a combinatorial optimization problem. However, for k > 1, this problem is NP-complete ([15]), and so unless P=NP, these approaches will take exponential amounts of time. Our approach differs significantly from these, in that it first attempts to find approximate conditional independencies by solving (polynomially many) submodular optimization problems, and then using a dynamic programming formulation to combine the approximate conditional independence information to derive a graphical model with underlying graph of the tree-width specified. This gives us an efficient (polynomial time in the number of random variables) PAC-learning algorithm which requires only polynomial number of samples of the true distribution, and only polynomial running time.

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

Connected through hyperedge HE-2 (induced by source paper "cheeger inequalities for submodular transformations"):
  with members:
    - "finding cheeger cuts in hypergraphs via heat equation"
    - "polynomial time algorithms for submodular laplacian systems"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "inhomogoenous hypergraph clustering with applications"
    - "spectral properties of hypergraph laplacian and approximation algorithms"
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "heat kernel based community detection"
    - "provable submodular minimization using wolfe s algorithm"

Connected through hyperedge HE-3 (induced by source paper "selective greedy equivalence search finding optimal bayesian networks using a polynomial number of score evaluations"):
  with members:
    - "a transformational characterization of equivalent bayesian network structures"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "parameterized complexity results for exact bayesian network structure learning"
    - "large sample learning of bayesian networks is np hard"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "finding optimal bayesian networks"
    - "on finding optimal polytrees"
    - "finding a path is harder than finding a tree"

Connected through hyperedge HE-4 (induced by source paper "parameterized complexity results for exact bayesian network structure learning"):
  with members:
    - "on the hardness of losing weight"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "a complete anytime algorithm for treewidth"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "algorithms and complexity results for exact bayesian structure learning"
    - "don t be strict in local search"

Connected through hyperedge HE-5 (induced by source paper "maximizing submodular functions using probabilistic graphical models"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "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"
    - "near optimal nonmyopic value of information in graphical models"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-6 (induced by source paper "learning factor graphs in polynomial time sample complexity"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "on the sample complexity of learning bayesian networks"
    - "efficient stepwise selection in decomposable models"
    - "efficiently inducing features of conditional random fields"
    - "finding optimal bayesian networks"
    - "finding a path is harder than finding a tree"

Connected through hyperedge HE-7 (induced by source paper "learning mixtures of submodular shells with application to document summarization"):
  with members:
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "learning determinantal point processes"
    - "a submodular supermodular procedure with applications to discriminative structure learning"
    - "near optimal nonmyopic value of information in graphical models"
    - "efficient minimization of decomposable submodular functions"
    - "bayesian query focused summarization"

Connected through hyperedge HE-8 (induced by source paper "convex relaxations for learning bounded treewidth decomposable graphs"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "efficient stepwise selection in decomposable models"
    - "generalized sequential tree reweighted message passing"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"
    - "near optimal nonmyopic value of information in graphical models"

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)