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: "where did my optimum go an empirical analysis of gradient descent optimization in policy gradient methods"
  Abstract: Recent analyses of certain gradient descent optimization methods have shown that performance can degrade in some settings - such as with stochasticity or implicit momentum. In deep reinforcement learning (Deep RL), such optimization methods are often used for training neural networks via the temporal difference error or policy gradient. As an agent improves over time, the optimization target changes and thus the loss landscape (and local optima) change. Due to the failure modes of those methods, the ideal choice of optimizer for Deep RL remains unclear. As such, we provide an empirical analysis of the effects that a wide range of gradient descent optimizers and their hyperparameters have on policy gradient methods, a subset of Deep RL algorithms, for benchmark continuous control tasks. We find that adaptive optimizers have a narrow window of effective learning rates, diverging in other cases, and that the effectiveness of momentum varies depending on the properties of the environment. Our analysis suggests that there is significant interplay between the dynamics of the environment and Deep RL algorithm properties which aren't necessarily accounted for by traditional adaptive gradient methods. We provide suggestions for optimal settings of current methods and further lines of research based on our findings.

Connected through hyperedge HE-1 (induced by source paper "fast efficient hyperparameter tuning for policy gradients"):
  with members:
    - "meta gradient reinforcement learning"
    - "bayesian optimization in alphago"
    - "deep variational reinforcement learning for pomdps"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "deep reinforcement learning that matters"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"
    - "towards generalization and simplicity in continuous control"

Connected through hyperedge HE-2 (induced by source paper "separating value functions across time scales"):
  with members:
    - "meta gradient reinforcement learning"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "are deep policy gradient algorithms truly policy gradient algorithms"
    - "deep reinforcement learning that matters"
    - "hybrid reward architecture for reinforcement learning"
    - "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "towards combining on off policy methods for real world applications"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "experience replay for continual learning"
    - "are deep policy gradient algorithms truly policy gradient algorithms"
    - "control of a quadrotor with reinforcement learning"
    - "time limits in reinforcement learning"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "exploration a study of count based exploration for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "understanding the impact of entropy on policy optimization"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "clipped action policy gradient"
    - "smoothed action value functions for learning gaussian policies"
    - "the goldilocks zone towards better understanding of neural network loss landscapes"
    - "addressing function approximation error in actor critic methods"
    - "are deep policy gradient algorithms truly policy gradient algorithms"
    - "equivalence between policy gradients and soft q 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.LG(Machine Learning)