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: "deep learning for reward design to improve monte carlo tree search in atari games"
  Abstract: Monte Carlo Tree Search (MCTS) methods have proven powerful in planning for sequential decision-making problems such as Go and video games, but their performance can be poor when the planning depth and sampling trajectories are limited or when the rewards are sparse. We present an adaptation of PGRD (policy-gradient for reward-design) for learning a reward-bonus function to improve UCT (a MCTS algorithm). Unlike previous applications of PGRD in which the space of reward-bonus functions was limited to linear functions of hand-coded state-action-features, we use PGRD with a multi-layer convolutional neural network to automatically learn features from raw perception as well as to adapt the non-linear reward-bonus function parameters. We also adopt a variance-reducing gradient method to improve PGRD's performance. The new method improves UCT's performance on multiple ATARI games compared to UCT without the reward bonus. Combining PGRD and Deep Learning in this way should make adapting rewards for MCTS algorithms far more widely and practically applicable than before.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning with goal distance gradient"):
  with members:
    - "deep multi agent reinforcement learning with discrete continuous hybrid action spaces"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "addressing function approximation error in actor critic methods"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "hindsight experience replay"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-2 (induced by source paper "what can learned intrinsic rewards capture"):
  with members:
    - "meta learning via learned loss"
    - "adapting behaviour via intrinsic reward a survey and empirical study"
    - "meta gradient reinforcement learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "on learning intrinsic rewards for policy gradient methods"
    - "meta learning update rules for unsupervised representation learning"
    - "learning to explore with meta policy gradient"
    - "learning a prior over intent via meta inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "how should an agent practice"):
  with members:
    - "unsupervised meta learning for reinforcement learning"
    - "meta gradient reinforcement learning"
    - "on learning intrinsic rewards for policy gradient methods"
    - "diversity is all you need learning skills without a reward function"
    - "promp proximal meta policy search"
    - "one shot visual imitation learning via meta learning"
    - "automatic goal generation for reinforcement learning agents"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "enforcing constraints for time series prediction in supervised unsupervised and reinforcement learning"):
  with members:
    - "enforcing constraints for interpolation and extrapolation in generative adversarial networks"
    - "neural ordinary differential equations"
    - "connecting generative adversarial networks and actor critic methods"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "evolved policy gradients"):
  with members:
    - "some considerations on learning to explore via meta reinforcement learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "equivalence between policy gradients and soft q learning"
    - "one shot visual imitation learning via meta learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "count based exploration with neural density models"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"

Connected through hyperedge HE-6 (induced by source paper "on learning intrinsic rewards for policy gradient methods"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "towards generalization and simplicity in continuous control"
    - "curiosity driven exploration by self supervised prediction"
    - "proximal policy optimization algorithms"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "concrete problems in ai safety"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "generative adversarial self imitation learning"):
  with members:
    - "self imitation learning"
    - "neural program synthesis with priority queue training"
    - "on learning intrinsic rewards for policy gradient methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "recall traces backtracking models for efficient reinforcement learning"
    - "neural episodic control"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-8 (induced by source paper "learning the reward function for a misspecified model"):
  with members:
    - "self supervised visual planning with temporal skip connections"
    - "value prediction network"
    - "recurrent environment simulators"
    - "self correcting models for model based reinforcement learning"
    - "value iteration networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "agnostic system identification for model based reinforcement learning"
    - "context tree switching"

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)