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: "the uncertainty bellman equation and exploration"
  Abstract: We consider the exploration/exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the expected value at subsequent time-steps. In this paper we consider a similar uncertainty Bellman equation (UBE), which connects the uncertainty at any time-step to the expected uncertainties at subsequent time-steps, thereby extending the potential exploratory benefit of a policy beyond individual time-steps. We prove that the unique fixed point of the UBE yields an upper bound on the variance of the estimated value of any fixed policy. This bound can be much tighter than traditional count-based bonuses that compound standard deviation rather than variance. Importantly, and unlike several existing approaches to optimism, this method scales naturally to large systems with complex generalization. Substituting our UBE-exploration strategy for $\epsilon$-greedy improves DQN performance on 51 out of 57 games in the Atari suite.

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 "reinforcement learning through active inference"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "deep active inference as variational policy gradients"
    - "a unified bellman optimality principle combining reward maximization and empowerment"
    - "deep dynamics models for learning dexterous manipulation"
    - "demystifying active inference"
    - "model based reinforcement learning for atari"
    - "efficient exploration via state marginal matching"
    - "dream to control learning behaviors by latent imagination"

Connected through hyperedge HE-3 (induced by source paper "kalman meets bellman improving policy evaluation through value tracking"):
  with members:
    - "a kernel loss for solving the bellman equation"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "successor uncertainties exploration and uncertainty in temporal difference learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shallow updates for deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "making sense of reinforcement learning and probabilistic inference"):
  with members:
    - "behaviour suite for reinforcement learning"
    - "virel a variational inference framework for reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "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"
    - "variational bayesian reinforcement learning with regret bounds"
    - "boltzmann exploration done right"
    - "a tutorial on thompson sampling"

Connected through hyperedge HE-5 (induced by source paper "efficient exploration of zero sum stochastic games"):
  with members:
    - "a distributional perspective on reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "learning to optimize via posterior sampling"
    - "a minimum relative entropy principle for learning and acting"

Connected through hyperedge HE-6 (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-7 (induced by source paper "qxplore q learning exploration by maximizing temporal difference error"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "go explore a new approach for hard exploration problems"
    - "q learning for continuous actions with cross entropy guided policies"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "exploration by random network distillation"
    - "ucb exploration via q ensembles"
    - "learning dexterous in hand manipulation"
    - "off policy deep reinforcement learning without exploration"

Connected through hyperedge HE-8 (induced by source paper "benchmarking bonus based exploration methods on the arcade learning environment"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "randomized value functions via multiplicative normalizing flows"
    - "large scale study of curiosity driven learning"
    - "dora the explorer directed outreaching reinforcement action selection"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"

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)