human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning factor graphs in polynomial time sample complexity"):
  Abstract: We study computational and sample complexity of parameter and structure learning in graphical models. Our main result shows that the class of factor graphs with bounded factor size and bounded connectivity can be learned in polynomial time and polynomial number of samples, assuming that the data is generated by a network in this class. This result covers both parameter estimation for a known network structure and structure learning. It implies as a corollary that we can learn factor graphs for both Bayesian networks and Markov networks of bounded degree, in polynomial time and sample complexity. Unlike maximum likelihood estimation, our method does not require inference in the underlying network, and so applies to networks where inference is intractable. We also show that the error of our learned model degrades gracefully when the generating distribution is not a member of the target class of networks.

Includes members:
  Member "maximum likelihood bounded tree width markov networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient learning of optimal markov network topology with k tree modeling")
  Member "on the sample complexity of learning bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "testing bayesian networks")
  Member "efficient stepwise selection in decomposable models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structure learning using forced pruning")
  Member "efficiently inducing features of conditional random fields":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geometric matrix completion with deep conditional random fields")
  Member "finding optimal bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "d vae a variational autoencoder for directed acyclic graphs")
  Member "pac learning bounded tree width graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient learning of optimal markov network topology with k tree modeling")
  Member "finding a path is harder than finding a tree":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "selective greedy equivalence search finding optimal bayesian networks using a polynomial number of score evaluations")

Question: Please tell me which class the source paper 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)