human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "count based exploration with the successor representation"
  Abstract: In this paper we introduce a simple approach for exploration in reinforcement learning (RL) that allows us to develop theoretically justified algorithms in the tabular case but that is also extendable to settings where function approximation is required. Our approach is based on the successor representation (SR), which was originally introduced as a representation defining state generalization by the similarity of successor states. Here we show that the norm of the SR, while it is being learned, can be used as a reward bonus to incentivize exploration. In order to better understand this transient behavior of the norm of the SR we introduce the substochastic successor representation (SSR) and we show that it implicitly counts the number of times each state (or feature) has been observed. We use this result to introduce an algorithm that performs as well as some theoretically sample-efficient approaches. Finally, we extend these ideas to a deep RL algorithm and show that it achieves state-of-the-art performance in Atari 2600 games when in a low sample-complexity regime.

Connected through hyperedge HE-1 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "torchbeast a pytorch platform for distributed rl"
    - "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"
    - "craftassist instruction parsing semantic parsing for a minecraft assistant"
    - "unsupervised discovery of decision states for transfer in reinforcement learning"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-2 (induced by source paper "optimistic exploration even with a pessimistic initialisation"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "exploration by random network distillation"
    - "learning latent dynamics for planning from pixels"
    - "dora the explorer directed outreaching reinforcement action selection"
    - "model based active exploration"
    - "is q learning provably efficient"
    - "directed exploration in pac model free reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "model primitive hierarchical lifelong reinforcement learning"):
  with members:
    - "information bottleneck and its applications in deep learning"
    - "continual lifelong learning with neural networks a review"
    - "norml no reward meta learning"
    - "dac the double actor critic architecture for learning options"
    - "reinforcement learning with competitive ensembles of information constrained primitives"
    - "deepmind control suite"
    - "safe option critic learning safety in the option critic architecture"
    - "neural processes"

Connected through hyperedge HE-4 (induced by source paper "scoring aggregating planning learning task agnostic priors from interactions and sparse rewards for zero shot generalization"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "mapping state space using landmarks for universal goal reaching"
    - "object oriented dynamics predictor"
    - "inverse reinforcement learning for video games"
    - "grasp2vec learning object representations from self supervised grasping"
    - "exploration by random network distillation"

Connected through hyperedge HE-5 (induced by source paper "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"):
  with members:
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "large scale study of curiosity driven learning"
    - "distributed prioritized experience replay"
    - "episodic curiosity through reachability"
    - "eigenoption discovery through the deep successor representation"
    - "feudal networks for hierarchical reinforcement learning"
    - "count based exploration with neural density models"

Connected through hyperedge HE-6 (induced by source paper "amplifying the imitation effect for reinforcement learning of ucav s mission execution"):
  with members:
    - "exploration by random network distillation"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "self imitation learning"
    - "diversity is all you need learning skills without a reward function"
    - "parameter space noise for exploration"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "count based exploration with neural density models"

Connected through hyperedge HE-7 (induced by source paper "marginalized state distribution entropy regularization in policy optimization"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "information asymmetry in kl regularized rl"
    - "provably efficient maximum entropy exploration"
    - "addressing function approximation error in actor critic methods"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "vfunc a deep generative model for functions"
    - "understanding the impact of entropy on policy optimization"

Connected through hyperedge HE-8 (induced by source paper "bayesian curiosity for efficient exploration in reinforcement learning"):
  with members:
    - "curious ilqr resolving uncertainty in model based rl"
    - "exploration by random network distillation"
    - "diversity is all you need learning skills without a reward function"
    - "efficient exploration through bayesian deep q networks"
    - "the uncertainty bellman equation and exploration"
    - "surprise based intrinsic motivation for deep reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "exploration a study of count based exploration for deep reinforcement learning"

Question: Please tell me which class the center node 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)