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: "finding optimal bayesian networks"
  Abstract: In this paper, we derive optimality results for greedy Bayesian-network search algorithms that perform single-edge modifications at each step and use asymptotically consistent scoring criteria. Our results extend those of Meek (1997) and Chickering (2002), who demonstrate that in the limit of large datasets, if the generative distribution is perfect with respect to a DAG defined over the observable variables, such search algorithms will identify this optimal (i.e. generative) DAG model. We relax their assumption about the generative distribution, and assume only that this distribution satisfies the {em composition property} over the observable variables, which is a more realistic assumption for real domains. Under this assumption, we guarantee that the search algorithms identify an {em inclusion-optimal} model; that is, a model that (1) contains the generative distribution and (2) has no sub-model that contains this distribution. In addition, we show that the composition property is guaranteed to hold whenever the dependence relationships in the generative distribution can be characterized by paths between singleton elements in some generative graphical model (e.g. a DAG, a chain graph, or a Markov network) even when the generative model includes unobserved variables, and even when the observed data is subject to selection bias.

Connected through hyperedge HE-1 (induced by source paper "d vae a variational autoencoder for directed acyclic graphs"):
  with members:
    - "dag gnn dag structure learning with graph neural networks"
    - "inductive matrix completion based on graph neural networks"
    - "survey on automated machine learning"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "detecting data errors with statistical constraints"):
  with members:
    - "bias in olap queries detection explanation and removal"
    - "discovering reliable approximate functional dependencies"
    - "holoclean holistic data repairs with probabilistic inference"
    - "qfix diagnosing errors through query histories"
    - "statistical distortion consequences of data cleaning"

Connected through hyperedge HE-3 (induced by source paper "solving all regression models for learning gaussian networks using givens rotations"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "online causal structure learning in the presence of latent variables"):
  with members:
    - "causal inference in the presence of latent variables and selection bias"
    - "decision theoretic foundations for causal reasoning"

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"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "learning factor graphs in polynomial time sample complexity"
    - "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 "evaluating causal models by comparing interventional distributions"):
  with members:
    - "testing identifiability of causal effects"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "bayesian structure learning using dynamic programming and mcmc"
    - "adjacency faithfulness and conservative causal inference"
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

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"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "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 "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"
    - "on finding optimal polytrees"
    - "pac learning bounded tree width graphical models"
    - "finding a path is harder than finding a tree"

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)