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: "incentivizing exploration in reinforcement learning with deep predictive models"
  Abstract: Achieving efficient and scalable exploration in complex domains poses a major challenge in reinforcement learning. While Bayesian and PAC-MDP approaches to the exploration problem offer strong formal guarantees, they are often impractical in higher dimensions due to their reliance on enumerating the state-action space. Hence, exploration in complex domains is often performed with simple epsilon-greedy methods. In this paper, we consider the challenging Atari games domain, which requires processing raw pixel inputs and delayed rewards. We evaluate several more sophisticated exploration strategies, including Thompson sampling and Boltzman exploration, and propose a new exploration method based on assigning exploration bonuses from a concurrently learned model of the system dynamics. By parameterizing our learned model with a neural network, we are able to develop a scalable and efficient approach to exploration bonuses that can be applied to tasks with complex, high-dimensional state spaces. In the Atari domain, our method provides the most consistent improvement across a range of games that pose a major challenge for prior methods. In addition to raw game-scores, we also develop an AUC-100 metric for the Atari Learning domain to evaluate the impact of exploration on this benchmark.

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 "never give up learning directed exploration strategies"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "mulex disentangling exploitation from exploration in deep rl"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "meta gradient reinforcement learning"
    - "an introduction to deep reinforcement learning"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"

Connected through hyperedge HE-3 (induced by source paper "intrinsic motivation for encouraging synergistic behavior"):
  with members:
    - "learning gentle object manipulation with curiosity driven deep reinforcement learning"
    - "self supervised exploration via disagreement"
    - "efficient bimanual manipulation using learned task schemas"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "emergent coordination through competition"
    - "large scale study of curiosity driven learning"
    - "emergence of structured behaviors from curiosity based intrinsic motivation"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-4 (induced by source paper "disentangling controllable object through video prediction improves visual reinforcement learning"):
  with members:
    - "model based reinforcement learning for atari"
    - "learning latent dynamics for planning from pixels"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "contingency aware exploration in reinforcement learning"
    - "independently controllable features"
    - "curiosity driven exploration by self supervised prediction"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-5 (induced by source paper "adversarial active exploration for inverse dynamics model learning"):
  with members:
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "parameter space noise for exploration"
    - "hindsight experience replay"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "learning how to active learn a deep reinforcement learning approach"
    - "robust adversarial reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "mime mutual information minimisation exploration"):
  with members:
    - "vase variational assorted surprise exploration for reinforcement learning"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "count based exploration with neural density models"
    - "surprise based intrinsic motivation for deep reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-7 (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-8 (induced by source paper "reinforcement learning in healthcare a survey"):
  with members:
    - "does the artificial intelligence clinician learn optimal treatment strategies for sepsis in intensive care"
    - "a dual mode adaptive basal bolus advisor based on reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "improving sepsis treatment strategies by combining deep and kernel based reinforcement learning"
    - "partial policy based reinforcement learning for anatomical landmark localization in 3d medical images"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "automatic view planning with multi scale deep reinforcement learning agents"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"

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)