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: "learning bayesian network structure from massive datasets the sparse candidate algorithm"
  Abstract: Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing learning tools address this optimization problem using standard heuristic search techniques. Since the search space is extremely large, such search procedures can spend most of the time examining candidates that are extremely unreasonable. This problem becomes critical when we deal with data sets that are large either in the number of instances, or the number of attributes. In this paper, we introduce an algorithm that achieves faster learning by restricting the search space. This iterative algorithm restricts the parents of each variable to belong to a small subset of candidates. We then search for a network that satisfies these constraints. The learned network is then used for selecting better candidates for the next iteration. We evaluate this algorithm both on synthetic and real-life data. Our results show that it is significantly faster than alternative search procedures without loss of quality in the learned structures.

Connected through hyperedge HE-1 (induced by source paper "sample debiasing in the themis open world database system extended version"):
  with members:
    - "idebench a benchmark for interactive data exploration"
    - "probabilistic database summarization for interactive data exploration"
    - "database learning toward a database that becomes smarter every time"
    - "estimating the impact of unknown unknowns on aggregate query results"
    - "sample selection bias correction theory"

Connected through hyperedge HE-2 (induced by source paper "evaluating structure learning algorithms with a balanced scoring function"):
  with members:
    - "improving accuracy and scalability of the pc algorithm by maximizing p value"
    - "scaling up greedy causal search for continuous variables"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal inference and causal explanation with background knowledge"
    - "adjacency faithfulness and conservative causal inference"

Connected through hyperedge HE-3 (induced by source paper "learning bayesian networks with low rank conditional probability tables"):
  with members:
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "bayesian network learning by compiling to weighted max sat"

Connected through hyperedge HE-4 (induced by source paper "learning binary bayesian networks in polynomial time and sample complexity"):
  with members:
    - "learning linear structural equation models in polynomial time and sample complexity"
    - "parameterized complexity results for exact bayesian network structure learning"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "bayesian network learning by compiling to weighted max sat"
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

Connected through hyperedge HE-5 (induced by source paper "rough set based lattice structure for knowledge representation in medical expert systems low back pain management case study"):
  with members:
    - "a framework for intelligent medical diagnosis using rough set with formal concept analysis"

Connected through hyperedge HE-6 (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"
    - "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"
    - "pac learning bounded tree width graphical models"
    - "finding a path is harder than finding a tree"

Connected through hyperedge HE-7 (induced by source paper "incorporating type ii error probabilities from independence tests into score based learning of bayesian network structure"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "on the sample complexity of learning bayesian networks"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "bayesian network learning with cutting planes"
    - "strong faithfulness and uniform consistency in causal inference"
    - "on the number of samples needed to learn the correct structure of a bayesian network"

Connected through hyperedge HE-8 (induced by source paper "advances in learning bayesian networks of bounded treewidth"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "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"
    - "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"

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)