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 in feature space for reinforcement learning"
  Abstract: We introduce a new count-based optimistic exploration algorithm for Reinforcement Learning (RL) that is feasible in environments with high-dimensional state-action spaces. The success of RL algorithms in these domains depends crucially on generalisation from limited training experience. Function approximation techniques enable RL agents to generalise in order to estimate the value of unvisited states, but at present few methods enable generalisation regarding uncertainty. This has prevented the combination of scalable RL algorithms with efficient exploration strategies that drive the agent to reduce its uncertainty. We present a new method for computing a generalised state visit-count, which allows the agent to estimate the uncertainty associated with any state. Our \phi-pseudocount achieves generalisation by exploiting same feature representation of the state space that is used for value function approximation. States that have less frequently observed features are deemed more uncertain. The \phi-Exploration-Bonus algorithm rewards the agent for exploring in feature space rather than in the untransformed state space. The method is simpler and less computationally expensive than some previous proposals, and achieves near state-of-the-art results on high-dimensional RL benchmarks.

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"
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (induced by source paper "exploring unknown states with action balance"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "a survey on intrinsic motivation in reinforcement learning"
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "provably efficient maximum entropy exploration"
    - "exploration by random network distillation"
    - "is q learning provably efficient"
    - "count based exploration with neural density models"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-3 (induced by source paper "go explore a new approach for hard exploration problems"):
  with members:
    - "fractal ai a fragile theory of intelligence"
    - "exploration by random network distillation"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-4 (induced by source paper "mega reward achieving human level play without extrinsic rewards"):
  with members:
    - "fast task inference with variational intrinsic successor features"
    - "diversity driven extensible hierarchical reinforcement learning"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "diversity is all you need learning skills without a reward function"
    - "generalisation of structural knowledge in the hippocampal entorhinal system"

Connected through hyperedge HE-5 (induced by source paper "learning efficient and effective exploration policies with counterfactual meta policy"):
  with members:
    - "exploration by random network distillation"
    - "meta gradient reinforcement learning"
    - "information directed exploration for deep reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "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"
    - "learning to explore with meta policy gradient"
    - "meta learning for contextual bandit exploration"

Connected through hyperedge HE-6 (induced by source paper "adapting behaviour via intrinsic reward a survey and empirical study"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "curiosity driven reinforcement learning with homeostatic regulation"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"

Connected through hyperedge HE-7 (induced by source paper "beyond exponentially discounted sum automatic learning of return function"):
  with members:
    - "separating value functions across time scales"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "relational deep reinforcement learning"
    - "meta gradient reinforcement learning"
    - "on learning intrinsic rewards for policy gradient methods"
    - "rudder return decomposition for delayed rewards"
    - "attention is all you need"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-8 (induced by source paper "a meta mdp approach to exploration for lifelong reinforcement learning"):
  with members:
    - "reinforcement learning for uav attitude control"
    - "multi advisor reinforcement learning"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "hybrid reward architecture for reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "a laplacian framework for option discovery in 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.AI(Artificial Intelligence)