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: "back to basics benchmarking canonical evolution strategies for playing atari"
  Abstract: Evolution Strategies (ES) have recently been demonstrated to be a viable alternative to reinforcement learning (RL) algorithms on a set of challenging deep RL problems, including Atari games and MuJoCo humanoid locomotion benchmarks. While the ES algorithms in that work belonged to the specialized class of natural evolution strategies (which resemble approximate gradient RL algorithms, such as REINFORCE), we demonstrate that even a very basic canonical ES algorithm can achieve the same or even better performance. This success of a basic ES algorithm suggests that the state-of-the-art can be advanced further by integrating the many advances made in the field of ES in the last decades. #R##N#We also demonstrate qualitatively that ES algorithms have very different performance characteristics than traditional RL algorithms: on some games, they learn to exploit the environment and perform much better while on others they can get stuck in suboptimal local minima. Combining their strengths with those of traditional RL algorithms is therefore likely to lead to new advances in the state of the art.

Connected through hyperedge HE-1 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges"):
  with members:
    - "example and feature importance based explanations for black box machine learning models"
    - "visual interpretability for deep learning a survey"
    - "controlling over generalization and its effect on adversarial examples detection and generation"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "counterexample guided data augmentation"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"

Connected through hyperedge HE-2 (induced by source paper "there are no bit parts for sign bits in black box attacks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "exploring the space of black box attacks on deep neural networks"
    - "signsgd compressed optimisation for non convex problems"

Connected through hyperedge HE-3 (induced by source paper "terminal prediction as an auxiliary task for deep reinforcement learning"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "backplay man muss immer umkehren"
    - "an introduction to deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "a deep learning approach for joint video frame and reward prediction in atari games"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "monte carlo tree search for policy optimization"):
  with members:
    - "evolution guided policy gradient in reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "safe mutations for deep and recurrent neural networks through output gradients"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "proximal policy optimization algorithms"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-5 (induced by source paper "evolvability es scalable and direct optimization of evolvability"):
  with members:
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "noisy networks for exploration"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"
    - "gradient estimation using stochastic computation graphs"
    - "evolvability is inevitable increasing evolvability without the pressure to adapt"

Connected through hyperedge HE-6 (induced by source paper "revenue relevance arbitrage and more joint optimization framework for search experiences in two sided marketplaces"):
  with members:
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"

Connected through hyperedge HE-7 (induced by source paper "policy search in continuous action domains an overview"):
  with members:
    - "hierarchical reinforcement learning with hindsight"
    - "data efficient hierarchical reinforcement learning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "first order and second order variants of the gradient descent a unified framework"
    - "state representation learning for control an overview"
    - "evolution guided policy gradient in reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "evolutionary stochastic gradient descent for optimization of deep neural networks"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "evolving deep neural networks"
    - "progressive neural architecture search"
    - "gradient free policy architecture search and adaptation"
    - "es is more than just a traditional finite difference approximator"
    - "regularizing and optimizing lstm language models"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for 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.NE(Neural and Evolutionary Computing)