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 networks with local structure"
  Abstract: In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability tables (CPTs), that quantify these networks. This increases the space of possible models, enabling the representation of CPTs with a variable number of parameters that depends on the learned local structures. The resulting learning procedure is capable of inducing models that better emulate the real complexity of the interactions present in the data. We describe the theoretical foundations and practical aspects of learning local structures, as well as an empirical evaluation of the proposed method. This evaluation indicates that learning curves characterizing the procedure that exploits the local structure converge faster than these of the standard procedure. Our results also show that networks learned with local structure tend to be more complex (in terms of arcs), yet require less parameters.

Connected through hyperedge HE-1 (induced by source paper "a meta transfer objective for learning to disentangle causal mechanisms"):
  with members:
    - "on first order meta learning algorithms"
    - "causal reasoning from meta reinforcement learning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "modular meta learning"
    - "the consciousness prior"
    - "learning independent causal mechanisms"
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-2 (induced by source paper "efficient learning of bounded treewidth bayesian networks from complete and incomplete data sets"):
  with members:
    - "the bayesian structural em algorithm"
    - "bayesian network learning with cutting planes"
    - "join graph propagation algorithms"

Connected through hyperedge HE-3 (induced by source paper "measuring adverse drug effects on multimorbity using tractable bayesian networks"):
  with members:
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "performing bayesian risk aggregation using discrete approximation algorithms with graph factorization"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "generalized belief propagation on tree robust structured region graphs"
    - "on the choice of regions for generalized belief propagation"
    - "structured region graphs morphing ep into gbp"
    - "convergent message passing algorithms a unifying view"
    - "join graph propagation algorithms"
    - "gaussian belief propagation based multiuser detection"
    - "truncating the loop series expansion for belief propagation"

Connected through hyperedge HE-5 (induced by source paper "fast learning of relational dependency networks"):
  with members:
    - "computing multi relational sufficient statistics for large databases"
    - "deep generative stochastic networks trainable by backprop"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "discriminative probabilistic models for relational data"
    - "finding optimal bayesian networks"
    - "closed form learning of markov networks from dependency networks"
    - "lifted graphical models a survey"
    - "cached sufficient statistics for efficient machine learning with large datasets"

Connected through hyperedge HE-6 (induced by source paper "representing and combining partially specified cpts"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "elicitation of probabilities for belief networks combining qualitative and quantitative information"
    - "constructing situation specific belief networks"
    - "advances in probabilistic reasoning"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "computing upper and lower bounds on likelihoods in intractable networks"

Connected through hyperedge HE-7 (induced by source paper "learning the structure of dynamic probabilistic networks"):
  with members:
    - "structure and parameter learning for causal independence and causal interaction models"
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "tractable inference for complex stochastic processes"
    - "the bayesian structural em algorithm"
    - "learning gaussian networks"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-8 (induced by source paper "mix nets factored mixtures of gaussians in bayesian networks with mixed continuous and discrete variables"):
  with members:
    - "nonuniform dynamic discretization in hybrid networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a multivariate discretization method for learning bayesian networks from mixed data"
    - "the anchors hierachy using the triangle inequality to survive high dimensional data"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "inference using message propagation and topology transformation in vector gaussian continuous networks"
    - "models and selection criteria for regression and classification"

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.AI(Artificial Intelligence)