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 effects of memory replay in reinforcement learning"
  Abstract: Experience replay is a key technique behind many recent advances in deep reinforcement learning. Allowing the agent to learn from earlier memories can speed up learning and break undesirable temporal correlations. Despite its wide-spread application, very little is understood about the properties of experience replay. How does the amount of memory kept affect learning dynamics? Does it help to prioritize certain experiences? In this paper, we address these questions by formulating a dynamical systems ODE model of Q-learning with experience replay. We derive analytic solutions of the ODE for a simple setting. We show that even in this very simple setting, the amount of memory kept can substantially affect the agent's performance. Too much or too little memory both slow down learning. Moreover, we characterize regimes where prioritized replay harms the agent's learning. We show that our analytic solutions have excellent agreement with experiments. Finally, we propose a simple algorithm for adaptively changing the memory buffer size which achieves consistently good empirical performance.

Connected through hyperedge HE-1 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction"):
  with members:
    - "gradient surgery for multi task learning"
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "towards characterizing divergence in deep q learning"
    - "behavior regularized offline reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "ray interference a source of plateaus in deep reinforcement learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a theoretical analysis of deep q learning"):
  with members:
    - "a theory of regularized markov decision processes"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "continual reinforcement learning in 3d non stationary environments"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "online meta learning"
    - "model primitive hierarchical lifelong reinforcement learning"
    - "task free continual learning"
    - "don t forget there is more than forgetting new metrics for continual learning"
    - "continuous learning in single incremental task scenarios"
    - "accelerated methods for deep reinforcement learning"
    - "state representation learning for control an overview"

Connected through hyperedge HE-5 (induced by source paper "experience replay optimization"):
  with members:
    - "remember and forget for experience replay"
    - "meta gradient reinforcement learning"
    - "on learning intrinsic rewards for policy gradient methods"
    - "organizing experience a deeper look at replay mechanisms for sample based planning in continuous state domains"
    - "learning to explore with meta policy gradient"
    - "selective experience replay for lifelong learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "hindsight experience replay"

Connected through hyperedge HE-6 (induced by source paper "adaptive genomic evolution of neural network topologies agent for state to action mapping in autonomous agents"):
  with members:
    - "advances in experience replay"
    - "multi criteria evolution of neural network topologies balancing experience and performance in autonomous systems"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "spectrum diverse neuroevolution with unified neural models"
    - "actor critic versus direct policy search a comparison based on sample complexity"
    - "continuous control with deep reinforcement learning"
    - "neuroevolution in games state of the art and open challenges"

Connected through hyperedge HE-7 (induced by source paper "a dual memory structure for efficient use of replay memory in deep reinforcement learning"):
  with members:
    - "sample efficient actor critic with experience replay"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"

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)