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: "large sample learning of bayesian networks is np hard"
  Abstract: In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a scoring criterion that favors the simplest model able to represent the generative distribution exactly. Our results therefore hold whenever the learning algorithm uses a consistent scoring criterion and is applied to a sufficiently large dataset. We show that identifying high-scoring structures is hard, even when we are given an independence oracle, an inference oracle, and/or an information oracle. Our negative results also apply to the learning of discrete-variable Bayesian networks in which each node has at most k parents, for all k > 3.

Connected through hyperedge HE-1 (induced by source paper "generalization in anti causal learning"):
  with members:
    - "generalization bounds for uniformly stable algorithms"
    - "deepcoder learning to write programs"
    - "attend infer repeat fast scene understanding with generative models"
    - "inferring deterministic causal relations"

Connected through hyperedge HE-2 (induced by source paper "semantically enhanced dynamic bayesian network for detecting sepsis mortality risk in icu patients with infection"):
  with members:
    - "toward automated early sepsis alerting identifying infection patients from nursing notes"
    - "expertbayes automatically refining manually built bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "emr based medical knowledge representation and inference via markov random fields and distributed representation learning"):
  with members:
    - "building a comprehensive syntactic and semantic corpus of chinese clinical texts"
    - "deep learning in neural networks"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "pgmhd a scalable probabilistic graphical model for massive hierarchical data problems"):
  with members:
    - "semantic measures for the comparison of units of language concepts or entities from text and knowledge base analysis"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-6 (induced by source paper "a greedy flexible algorithm to learn an optimal bayesian network structure"):
  with members:
    - "a simple approach for finding the globally optimal bayesian network structure"

Connected through hyperedge HE-7 (induced by source paper "a simple approach for finding the globally optimal bayesian network structure"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "a transformational characterization of equivalent bayesian network structures"

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)