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: "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
  Abstract: Policy gradient methods in reinforcement learning have become increasingly prevalent for state-of-the-art performance in continuous control tasks. Novel methods typically benchmark against a few key algorithms such as deep deterministic policy gradients and trust region policy optimization. As such, it is important to present and use consistent baselines experiments. However, this can be difficult due to general variance in the algorithms, hyper-parameter tuning, and environment stochasticity. We investigate and discuss: the significance of hyper-parameters in policy gradients for continuous control, general variance in the algorithms, and reproducibility of reported results. We provide guidelines on reporting novel results as comparisons against baseline methods such that future researchers can make informed decisions when investigating novel methods.

Connected through hyperedge HE-1 (induced by source paper "striving for simplicity and performance in off policy drl output normalization and non uniform sampling"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "benchmarking model based reinforcement learning"
    - "remember and forget for experience replay"
    - "supervised policy update for deep reinforcement learning"
    - "clipped action policy gradient"
    - "marginal policy gradients a unified family of estimators for bounded action spaces with applications"
    - "curiosity driven experience prioritization via density estimation"
    - "safe exploration in continuous action spaces"

Connected through hyperedge HE-2 (induced by source paper "automatic neural network compression by sparsity quantization joint learning a constrained optimization based approach"):
  with members:
    - "single path one shot neural architecture search with uniform sampling"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "ecc energy constrained deep neural network compression via a bilinear regression model"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "energy constrained compression for deep neural networks via weighted sparse projection and layer input masking"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "chainerrl a deep reinforcement learning library"):
  with members:
    - "chainer a deep learning framework for accelerating the research cycle"
    - "catalyst rl a distributed framework for reproducible rl research"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "dopamine a research framework for deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-4 (induced by source paper "benchmarking model based reinforcement learning"):
  with members:
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "deepmind control suite"
    - "model ensemble trust region policy optimization"
    - "learning dexterous in hand manipulation"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-5 (induced by source paper "collaborative evolutionary reinforcement learning"):
  with members:
    - "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"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning that matters"
    - "parameter space noise for exploration"
    - "hindsight experience replay"
    - "count based exploration with neural density models"

Connected through hyperedge HE-6 (induced by source paper "boosting soft actor critic emphasizing recent experience without forgetting the past"):
  with members:
    - "remember and forget for experience replay"
    - "curiosity driven experience prioritization via density estimation"
    - "soft actor critic algorithms and applications"
    - "vizdoom drqn with prioritized experience replay double q learning snapshot ensembling"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "high dimensional control using generalized auxiliary tasks"):
  with members:
    - "samples are not all useful denoising policy gradient updates using variance"
    - "terminal prediction as an auxiliary task for deep reinforcement learning"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "large scale study of curiosity driven learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "recurrent world models facilitate policy evolution"
    - "bridging the gap between value and policy based reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "catalyst rl a distributed framework for reproducible rl research"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "deterministic implementations for reproducibility in deep reinforcement learning"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "latent space policies for hierarchical reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning that matters"

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)