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 "efficient learning and planning with compressed predictive states"):
  Abstract: Predictive state representations (PSRs) offer an expressive framework for modelling partially observable systems. By compactly representing systems as functions of observable quantities, the PSR learning approach avoids using local-minima prone expectation-maximization and instead employs a globally optimal moment-based algorithm. Moreover, since PSRs do not require a predetermined latent state structure as an input, they offer an attractive framework for model-based reinforcement learning when agents must plan without a priori access to a system model. Unfortunately, the expressiveness of PSRs comes with significant computational cost, and this cost is a major factor inhibiting the use of PSRs in applications. In order to alleviate this shortcoming, we introduce the notion of compressed PSRs (CPSRs). The CPSR learning approach combines recent advancements in dimensionality reduction, incremental matrix decomposition, and compressed sensing. We show how this approach provides a principled avenue for learning accurate approximations of PSRs, drastically reducing the computational costs associated with learning while also providing effective regularization. Going further, we propose a planning framework which exploits these learned models. And we show that this approach facilitates model-learning and planning in large complex partially observable domains, a task that is infeasible without the principled use of compression.

Includes members:
  Member "hilbert space embeddings of predictive state representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning causal state representations of partially observable environments")
  Member "pegasus a policy search method for large mdps and pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fidi rl incorporating deep reinforcement learning with finite difference policy search for efficient learning of continuous control")
  Member "predictive state representations a new theory for modeling dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discovery of useful questions as auxiliary tasks")
  Member "local loss optimization in operator models a new insight into spectral learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a maximum matching algorithm for basis selection in spectral learning")
  Member "a spectral learning approach to range only slam":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locdyn robust distributed localization for mobile underwater networks")
  Member "a method of moments for mixture models and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provable meta learning of linear representations")
  Member "bellman error based feature generation using random projections on sparse spaces":
    also connected through other hyperedges:
      (none)
  Member "predictive state temporal difference learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to filter with predictive state inference machines")

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)