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: "sparsityboost a new scoring function for learning bayesian network structure"
  Abstract: We give a new consistent scoring function for structure learning of Bayesian networks. In contrast to traditional approaches to scorebased structure learning, such as BDeu or MDL, the complexity penalty that we propose is data-dependent and is given by the probability that a conditional independence test correctly shows that an edge cannot exist. What really distinguishes this new scoring function from earlier work is that it has the property of becoming computationally easier to maximize as the amount of data increases. We prove a polynomial sample complexity result, showing that maximizing this score is guaranteed to correctly learn a structure with no false edges and a distribution close to the generating distribution, whenever there exists a Bayesian network which is a perfect map for the data generating distribution. Although the new score can be used with any search algorithm, we give empirical results showing that it is particularly effective when used together with a linear programming relaxation approach to Bayesian network structure learning.

Connected through hyperedge HE-1 (induced by source paper "computationally and statistically efficient learning of causal bayes nets using path queries"):
  with members:
    - "reconstructing a bounded degree directed tree using path queries"
    - "learning causal graphs with small interventions"
    - "powers of tensors and fast matrix multiplication"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-2 (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"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "parameterized complexity results for exact bayesian network structure learning"
    - "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-3 (induced by source paper "exposing the probabilistic causal structure of discrimination"):
  with members:
    - "theory refinement on bayesian networks"
    - "fairness through awareness"

Connected through hyperedge HE-4 (induced by source paper "on the sample complexity of learning graphical games"):
  with members:
    - "from behavior to sparse graphical games efficient recovery of equilibria"
    - "on influence stable behavior and the most influential individuals in networks a game theoretic approach"
    - "graphical models for game theory"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "a continuation method for nash equilibria in structured games"

Connected through hyperedge HE-5 (induced by source paper "information theoretic limits of bayesian network structure learning"):
  with members:
    - "on the sample complexity of learning bayesian networks"
    - "learning factor graphs in polynomial time sample complexity"
    - "finding optimal bayesian networks"
    - "on the number of samples needed to learn the correct structure of a bayesian network"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-6 (induced by source paper "on the sample complexity of learning sparse graphical games"):
  with members:
    - "graphical models for game theory"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "a continuation method for nash equilibria in structured games"

Connected through hyperedge HE-7 (induced by source paper "information theoretic limits of learning the structure of bayesian networks"):
  with members:
    - "on the sample complexity of learning bayesian networks"
    - "finding optimal bayesian networks"
    - "on the number of samples needed to learn the correct structure of a bayesian network"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-8 (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"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "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"

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)