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: "shallow updates for deep reinforcement learning"
  Abstract: Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN) have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This success is mainly attributed to the power of deep neural networks to learn rich domain representations for approximating the value function or policy. Batch reinforcement learning methods with linear representations, on the other hand, are more stable and require less hyper parameter tuning. Yet, substantial feature engineering is necessary to achieve good results. In this work we propose a hybrid approach -- the Least Squares Deep Q-Network (LS-DQN), which combines rich feature representations learned by a DRL algorithm with the stability of a linear least squares method. We do this by periodically re-training the last hidden layer of a DRL network with a batch least squares update. Key to our approach is a Bayesian regularization term for the least squares update, which prevents over-fitting to the more recent data. We tested LS-DQN on five Atari games and demonstrate significant improvement over vanilla DQN and Double-DQN. We also investigated the reasons for the superior performance of our method. Interestingly, we found that the performance improvement can be attributed to the large batch size used by the LS method when optimizing the last layer.

Connected through hyperedge HE-1 (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"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "the uncertainty bellman equation and exploration"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a geometric perspective on optimal representations for reinforcement learning"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "the value function polytope in reinforcement learning"
    - "the laplacian in rl learning representations with efficient approximations"
    - "dopamine a research framework for deep reinforcement learning"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "combined reinforcement learning via abstract representations"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"

Connected through hyperedge HE-3 (induced by source paper "trust region value optimization using kalman filtering"):
  with members:
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "a distributional perspective on reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "an overview of gradient descent optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "deep neural linear bandits overcoming catastrophic forgetting through likelihood matching"):
  with members:
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "efficient exploration through bayesian deep q networks"
    - "the uncertainty bellman equation and exploration"
    - "a tutorial on thompson sampling"
    - "curiosity driven exploration by self supervised prediction"
    - "graying the black box understanding dqns"
    - "overcoming catastrophic forgetting in neural networks"
    - "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce"

Connected through hyperedge HE-5 (induced by source paper "efficient exploration through bayesian deep q networks"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "deep reinforcement learning that matters"
    - "equivalence between policy gradients and soft q learning"
    - "count based exploration with neural density models"
    - "noisy networks for exploration"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "a unified view of entropy regularized markov decision processes"

Connected through hyperedge HE-6 (induced by source paper "learn what not to learn action elimination with deep reinforcement learning"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "textworld a learning environment for text based games"
    - "counting to explore and generalize in text based games"
    - "efficient exploration through bayesian deep q networks"
    - "end to end task completion neural dialogue systems"
    - "a deep reinforcement learning chatbot"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-7 (induced by source paper "successor uncertainties exploration and uncertainty in temporal difference learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "count based exploration with the successor representation"
    - "randomized value functions via multiplicative normalizing flows"
    - "information directed exploration for deep reinforcement learning"
    - "efficient exploration through bayesian deep q networks"
    - "parameter space noise for exploration"
    - "eigenoption discovery through the deep successor representation"
    - "the uncertainty bellman equation and exploration"

Connected through hyperedge HE-8 (induced by source paper "context dependent upper confidence bounds for directed exploration"):
  with members:
    - "parameter space noise for exploration"
    - "count based exploration with neural density models"
    - "count based exploration in feature space for reinforcement learning"
    - "bounded optimal exploration in mdp"
    - "deep exploration via bootstrapped dqn"
    - "bayesian optimal control of smoothly parameterized systems the lazy posterior sampling algorithm"
    - "gaussian processes for sample efficient reinforcement learning with rmax like exploration"
    - "dyna style planning with linear function approximation and prioritized sweeping"

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)