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 "state aggregation learning from markov transition data"):
  Abstract: State aggregation is a model reduction method rooted in control theory and reinforcement learning. It reduces the complexity of engineering systems by mapping the system's states into a small number of meta-states. In this paper, we study the unsupervised estimation of unknown state aggregation structures based on Markov trajectories. We formulate the state aggregation of Markov processes into a nonnegative factorization model, where left and right factor matrices correspond to aggregation and disaggregation distributions respectively. By leveraging techniques developed in the context of topic modeling, we propose an efficient polynomial-time algorithm for computing the estimated state aggregation model. Under some "anchor state" assumption, we show that one can reliably recover the state aggregation structure from sample transitions with high probability. Sharp divergence error bounds are proved for the estimated aggregation and disaggregation distributions, and experiments with Manhattan traffic data are provided.

Includes members:
  Member "sample optimal parametric q learning with linear transition models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "on learning markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mcdiarmid type inequalities for graph dependent variables and stability bounds")
  Member "scalable bilinear pi learning using state and action features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reinforcement learning via fenchel rockafellar duality")
  Member "learning arbitrary statistical mixtures of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal estimation of change in a population of parameters")
  Member "on learning parametric output hmms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "divergence based motivation for online em and combining hidden variable models")
  Member "learning mixtures of arbitrary distributions over large discrete domains":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning sums of independent random variables with sparse collective support")
  Member "learning topic models going beyond svd":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "contrastive estimation reveals topic posterior information to linear 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)