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 "successor features combine elements of model free and model based reinforcement learning"):
  Abstract: A key question in reinforcement learning is how an intelligent agent can generalize knowledge across different inputs. By generalizing across different inputs, information learned for one input can be immediately reused for improving predictions for another input. Reusing information allows an agent to compute an optimal decision-making strategy using less data. State representation is a key element of the generalization process, compressing a high-dimensional input space into a low-dimensional latent state space. This article analyzes properties of different latent state spaces, leading to new connections between model-based and model-free reinforcement learning. Successor features, which predict frequencies of future observations, form a link between model-based and model-free learning: Learning to predict future expected reward outcomes, a key characteristic of model-based agents, is equivalent to learning successor features. Learning successor features is a form of temporal difference learning and is equivalent to learning to predict a single policy's utility, which is a characteristic of model-free agents. Drawing on the connection between model-based reinforcement learning and successor features, we demonstrate that representations that are predictive of future reward outcomes generalize across variations in both transitions and rewards. This result extends previous work on successor features, which is constrained to fixed transitions and assumes re-learning of the transferred state representation.

Includes members:
  Member "deepmdp learning continuous latent space models for representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neuroevolution of self interpretable agents")
  Member "combined reinforcement learning via abstract representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "learning the reward function for a misspecified model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reward estimation for variance reduction in deep reinforcement learning")
  Member "lipschitz continuity in model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep rbf value functions for continuous control")
  Member "is q learning provably efficient":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "value prediction network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "near optimal behavior via approximate state abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning state abstractions for transfer in continuous control")
  Member "advantages and limitations of using successor features for transfer in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "successor features support model based and model free reinforcement learning")

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)