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 "exploring unknown states with action balance"):
  Abstract: Exploration is a key problem in reinforcement learning. Recently bonus-based methods have achieved considerable successes in environments where exploration is difficult such as Montezuma's Revenge, which assign additional bonus (e.g., intrinsic reward) to guide the agent to rarely visited states. Since the bonus is calculated according to the novelty of the next state after performing an action, we call such methods the next-state bonus methods. However, the next-state bonus methods bring extra issues. It may lead agent to be trapped in states that fewer being visited and ignore to explore unknown states. Moreover, the behavior policy of the agent is also influenced by the bonus added to the state (or state-action) values indirectly. In contrast to the bonus-based methods which explore in known states, in this paper, we focus on the other part of exploration: exploration for finding unknown states. We propose the action balance exploration method to overcome the defects of the next-state bonus methods, which balances the chosen time of each action in each state and can be treated as an extension of upper confidence bound (UCB) to deep reinforcement learning. To take both the advantages of the next-state bonus method and our action balance exploration method, we propose the action balance RND method, which takes both parts of exploration into consideration. The experiments on grid world and Atari games demonstrate action balance exploration has a better capability in finding unknown states and can improve the real performance of RND in some hard exploration environments respectively.

Includes members:
  Member "go explore a new approach for hard exploration problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "a survey on intrinsic motivation in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "social navigation with human empowerment driven reinforcement learning")
  Member "benchmarking bonus based exploration methods on the arcade learning environment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta learning curiosity algorithms")
  Member "provably efficient maximum entropy exploration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "exploration by random network distillation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "is q learning provably efficient":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "count based exploration with neural density models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "count based exploration in feature space for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")

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.AI(Artificial Intelligence)