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: "computing posterior probabilities of structural features in bayesian networks"
  Abstract: We study the problem of learning Bayesian network structures from data. Koivisto and Sood (2004) and Koivisto (2006) presented algorithms that can compute the exact marginal posterior probability of a subnetwork, e.g., a single edge, in O(n2n) time and the posterior probabilities for all n(n-1) potential edges in O(n2n) total time, assuming that the number of parents per node or the indegree is bounded by a constant. One main drawback of their algorithms is the requirement of a special structure prior that is non uniform and does not respect Markov equivalence. In this paper, we develop an algorithm that can compute the exact posterior probability of a subnetwork in O(3n) time and the posterior probabilities for all n(n-1) potential edges in O(n3n) total time. Our algorithm also assumes a bounded indegree but allows general structure priors. We demonstrate the applicability of the algorithm on several data sets with up to 20 variables.

Connected through hyperedge HE-1 (induced by source paper "fast multi subset transform and weighted sums over acyclic digraphs"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "counting thin subgraphs via packings faster than meet in the middle time"

Connected through hyperedge HE-2 (induced by source paper "a parallel algorithm for exact bayesian structure discovery in bayesian networks"):
  with members:
    - "exact structure discovery in bayesian networks with less space"
    - "an improved admissible heuristic for learning optimal bayesian networks"
    - "advances in exact bayesian structure discovery in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "bayesian model averaging using the k best bayesian network structures"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal discovery from a mixture of experimental and observational data"
    - "data analysis with bayesian networks a bootstrap approach"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "bayesian structure learning using dynamic programming and mcmc"
    - "advances in exact bayesian structure discovery in bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "partial order mcmc for structure discovery in bayesian networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "bayesian structure learning using dynamic programming and mcmc"
    - "advances in exact bayesian structure discovery in bayesian networks"

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)