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 "online gaussian process state space models learning and planning for partially observable dynamical systems"):
  Abstract: Gaussian process state-space model (GPSSM) is a probabilistic dynamical system that represents unknown transition and/or measurement models as the Gaussian process (GP). The majority of approaches to learning GPSSM are focused on handling given time series data. However, in most dynamical systems, data required for model learning arrives sequentially and accumulates over time. Storing all the data requires large ammounts of memory, and using it for model learning can be computationally infeasible. To overcome this challenge, this paper develops an online inference method for learning the GPSSM (onlineGPSSM) that fuses stochastic variational inference (VI) and online VI. The proposed method can mitigate the computation time issue without catastrophic forgetting and supports adaptation to changes in a system and/or a real environments. Furthermore, we propose a GPSSM-based reinforcement learning (RL) framework for partially observable dynamical systems by combining onlineGPSSM with Bayesian filtering and trajectory optimization algorithms. Numerical examples are presented to demonstrate the applicability of the proposed method.

Includes members:
  Member "model based reinforcement learning with parametrized physical models and optimism driven exploration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scoring aggregating planning learning task agnostic priors from interactions and sparse rewards for zero shot generalization")
  Member "variational gaussian process state space models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fedloc federated learning framework for cooperative localization and location data processing")

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.RO(Robotics)