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 overcomplete hmms"):
  Abstract: We study the problem of learning overcomplete HMMs---those that have many hidden states but a small output alphabet. Despite having significant practical importance, such HMMs are poorly understood with no known positive or negative results for efficient learning. In this paper, we present several new results---both positive and negative---which help define the boundaries between the tractable and intractable settings. Specifically, we show positive results for a large subclass of HMMs whose transition matrices are sparse, well-conditioned, and have small probability mass on short cycles. On the other hand, we show that learning is impossible given only a polynomial number of samples for HMMs with a small output alphabet and whose transition matrices are random regular graphs with large degree. We also discuss these results in the context of learning HMMs which can capture long-term dependencies.

Includes members:
  Member "prediction with a short memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning deterministic weighted automata with queries and counterexamples")
  Member "learning parametric output hmms with two aliased states":
    also connected through other hyperedges:
      (none)
  Member "minimal realization problems for hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "smoothed analysis in unsupervised learning via decoupling")
  Member "smoothed analysis of tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improper learning for non stochastic control")
  Member "uniqueness of tensor decompositions with applications to polynomial identifiability":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "smoothed analysis in unsupervised learning via decoupling")
  Member "a method of moments for mixture models and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provable meta learning of linear representations")
  Member "learning nonsingular phylogenies and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "guaranteed scalable learning of latent tree models")

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)