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 some popular gaussian graphical models without condition number bounds"):
  Abstract: Gaussian Graphical Models (GGMs) have wide-ranging applications in machine learning and the natural and social sciences. In most of the settings in which they are applied, the number of observed samples is much smaller than the dimension and they are assumed to be sparse. While there are a variety of algorithms (e.g. Graphical Lasso, CLIME) that provably recover the graph structure with a logarithmic number of samples, they assume various conditions that require the precision matrix to be in some sense well-conditioned. #R##N#Here we give the first polynomial-time algorithms for learning attractive GGMs and walk-summable GGMs with a logarithmic number of samples without any such assumptions. In particular, our algorithms can tolerate strong dependencies among the variables. We complement our results with experiments showing that many existing algorithms fail even in some simple settings where there are long dependency chains, whereas ours do not.

Includes members:
  Member "reducibility and computational lower bounds for problems with planted sparse structure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarially robust low dimensional representations")
  Member "learning graphical models using multiplicative weights":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privately learning markov random fields")
  Member "information theoretic optimal learning of gaussian graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient learning of discrete graphical models")
  Member "interaction screening efficient and sample optimal learning of ising models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privately learning markov random fields")
  Member "learning without concentration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning with correntropy induced losses for regression with mixture of symmetric stable noise")
  Member "efficiently learning ising models on arbitrary graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privately learning markov random fields")
  Member "towards a mathematical theory of super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "subset selection for gaussian markov random fields":
    also connected through other hyperedges:
      (none)

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)