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: "collaborative evolutionary reinforcement learning"
  Abstract: Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically struggle with achieving effective exploration and are extremely sensitive to the choice of hyperparameters. One reason is that most approaches use a noisy version of their operating policy to explore - thereby limiting the range of exploration. In this paper, we introduce Collaborative Evolutionary Reinforcement Learning (CERL), a scalable framework that comprises a portfolio of policies that simultaneously explore and exploit diverse regions of the solution space. A collection of learners - typically proven algorithms like TD3 - optimize over varying time-horizons leading to this diverse portfolio. All learners contribute to and use a shared replay buffer to achieve greater sample efficiency. Computational resources are dynamically distributed to favor the best learners as a form of online algorithm selection. Neuroevolution binds this entire process to generate a single emergent learner that exceeds the capabilities of any individual learner. Experiments in a range of continuous control benchmarks demonstrate that the emergent learner significantly outperforms its composite learners while remaining overall more sample-efficient - notably solving the Mujoco Humanoid benchmark where all of its composite learners (TD3) fail entirely in isolation.

Connected through hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning"):
  with members:
    - "novelty search for deep reinforcement learning policy network weights by action sequence edit metric distance"
    - "provably robust blackbox optimization for reinforcement learning"
    - "behavior guided reinforcement learning"
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "adapting behaviour for learning progress"
    - "attraction repulsion actor critic for continuous control reinforcement learning"
    - "a generalized framework for population based training"
    - "emergent coordination through competition"

Connected through hyperedge HE-2 (induced by source paper "multi path policy optimization"):
  with members:
    - "diversity inducing policy gradient using maximum mean discrepancy to find a set of diverse policies"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "path consistency learning in tsallis entropy regularized mdps"
    - "addressing function approximation error in actor critic methods"
    - "policy search in continuous action domains an overview"

Connected through hyperedge HE-3 (induced by source paper "evolutionary reinforcement learning for sample efficient multiagent coordination"):
  with members:
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "emergent coordination through competition"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "pommerman a multi agent playground"
    - "counterfactual multi agent policy gradients"
    - "a multi agent reinforcement learning model of common pool resource appropriation"

Connected through hyperedge HE-4 (induced by source paper "attraction repulsion actor critic for continuous control reinforcement learning"):
  with members:
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "randomized value functions via multiplicative normalizing flows"
    - "evolution guided policy gradient in reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "boosting trust region policy optimization by normalizing flows policy"
    - "bayesian policy gradients via alpha divergence dropout inference"

Connected through hyperedge HE-5 (induced by source paper "coordinated exploration via intrinsic rewards for multi agent reinforcement learning"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "exploration by random network distillation"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "counterfactual multi agent policy gradients"
    - "count based exploration with neural density models"

Connected through hyperedge HE-6 (induced by source paper "recruitment imitation mechanism for evolutionary reinforcement learning"):
  with members:
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "evolution guided policy gradient in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "comparing deep reinforcement learning and evolutionary methods in continuous control"
    - "deep reinforcement learning that matters"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "robust opponent modeling via adversarial ensemble reinforcement learning in asymmetric imperfect information games"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "emergent complexity via multi agent competition"
    - "population based training of neural networks"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "proximal distilled evolutionary reinforcement learning"):
  with members:
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "evolution guided policy gradient in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "parameter space noise for exploration"
    - "hindsight experience replay"
    - "safe mutations for deep and recurrent neural networks through output gradients"
    - "proximal policy optimization algorithms"
    - "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.LG(Machine Learning)