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 reinforcement learning and the deadly triad"
  Abstract: We know from reinforcement learning theory that temporal difference learning can fail in certain cases. Sutton and Barto (2018) identify a deadly triad of function approximation, bootstrapping, and off-policy learning. When these three properties are combined, learning can diverge with the value estimates becoming unbounded. However, several algorithms successfully combine these three properties, which indicates that there is at least a partial gap in our understanding. In this work, we investigate the impact of the deadly triad in practice, in the context of a family of popular deep reinforcement learning models - deep Q-networks trained with experience replay - analysing how the components of this system play a role in the emergence of the deadly triad, and in the agent's performance

Connected through hyperedge HE-1 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online"):
  with members:
    - "meta learning representations for continual learning"
    - "learning sparse representations in reinforcement learning"
    - "learning action representations for reinforcement learning"
    - "representation learning on graphs a reinforcement learning application"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "two geometric input transformation methods for fast online reinforcement learning with neural nets"
    - "the utility of sparse representations for control in reinforcement learning"
    - "learning sparse representations in reinforcement learning with sparse coding"

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 "when to use parametric models in reinforcement learning"):
  with members:
    - "planning with expectation models"
    - "model based reinforcement learning for atari"
    - "on inductive biases in deep reinforcement learning"
    - "an online learning approach to model predictive control"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "rainbow combining improvements in deep reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "noisy networks for exploration"

Connected through hyperedge HE-4 (induced by source paper "behavior regularized offline reinforcement learning"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "off policy evaluation via off policy classification"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "breaking the curse of horizon infinite horizon off policy estimation"

Connected through hyperedge HE-5 (induced by source paper "deep tile coder an efficient sparse representation learning approach with applications in reinforcement learning"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "learning sparse representations in reinforcement learning"
    - "learning action representations for reinforcement learning"
    - "representation learning on graphs a reinforcement learning application"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "soft actor critic algorithms and applications"
    - "two geometric input transformation methods for fast online reinforcement learning with neural nets"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-6 (induced by source paper "diagnosing bottlenecks in deep q learning algorithms"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "soft actor critic algorithms and applications"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "the utility of sparse representations for control in reinforcement learning"
    - "policy optimization via importance sampling"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

Connected through hyperedge HE-7 (induced by source paper "training agents using upside down reinforcement learning"):
  with members:
    - "reinforcement learning upside down don t predict rewards just map them to actions"
    - "temporal difference models model free deep rl for model based control"
    - "self imitation learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "tune a research platform for distributed model selection and training"
    - "hindsight experience replay"
    - "hindsight policy gradients"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "the problem with ddpg understanding failures in deterministic environments with sparse rewards"):
  with members:
    - "q learning for continuous actions with cross entropy guided policies"
    - "towards characterizing divergence in deep q learning"
    - "exploiting the sign of the advantage function to learn deterministic policies in continuous domains"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"

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)