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: "structured control nets for deep reinforcement learning"
  Abstract: In recent years, Deep Reinforcement Learning has made impressive advances in solving several important benchmark problems for sequential decision making. Many control applications use a generic multilayer perceptron (MLP) for non-vision parts of the policy network. In this work, we propose a new neural network architecture for the policy network representation that is simple yet effective. The proposed Structured Control Net (SCN) splits the generic MLP into two separate sub-modules: a nonlinear control module and a linear control module. Intuitively, the nonlinear control is for forward-looking and global control, while the linear control stabilizes the local dynamics around the residual of global control. We hypothesize that this will bring together the benefits of both linear and nonlinear policies: improve training sample efficiency, final episodic reward, and generalization of learned policy, while requiring a smaller network and being generally applicable to different training methods. We validated our hypothesis with competitive results on simulations from OpenAI MuJoCo, Roboschool, Atari, and a custom 2D urban driving environment, with various ablation and generalization tests, trained with multiple black-box and policy gradient training methods. The proposed architecture has the potential to improve upon broader control tasks by incorporating problem specific priors into the architecture. As a case study, we demonstrate much improved performance for locomotion tasks by emulating the biological central pattern generators (CPGs) as the nonlinear part of the architecture.

Connected through hyperedge HE-1 (induced by source paper "taurus an intelligent data plane"):
  with members:
    - "scaling distributed machine learning with in network aggregation"
    - "meta learning neural bloom filters"
    - "fpga based accelerators of deep learning networks for learning and classification a review"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "experience driven networking a deep reinforcement learning based approach"
    - "in network neural networks"
    - "training deep neural networks with 8 bit floating point numbers"
    - "how many mice make an elephant modelling flow length and size distribution of internet traffic"

Connected through hyperedge HE-2 (induced by source paper "generalization in reinforcement learning with selective noise injection and information bottleneck"):
  with members:
    - "rogue gym a new challenge for generalization in reinforcement learning"
    - "deep reinforcement learning on a budget 3d control and reasoning without a supercomputer"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "natural environment benchmarks for reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "assessing generalization in deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "regularization matters in policy optimization"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "control regularization for reduced variance reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "generalization and regularization in dqn"
    - "meta reinforcement learning of structured exploration strategies"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "generalization in transfer learning"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "benchmark environments for multitask learning in continuous domains"
    - "deep reinforcement learning that matters"
    - "progressive neural networks"

Connected through hyperedge HE-5 (induced by source paper "investigating generalisation in continuous deep reinforcement learning"):
  with members:
    - "deepmind control suite"
    - "the limits and potentials of deep learning for robotics"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "assessing generalization in deep reinforcement learning"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-6 (induced by source paper "recurrent control nets for deep reinforcement learning"):
  with members:
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep residual learning for image recognition"
    - "trust region policy optimization"

Connected through hyperedge HE-7 (induced by source paper "learning extreme hummingbird maneuvers on flapping wing robots"):
  with members:
    - "flappy hummingbird an open source dynamic simulation of flapping wing robots and animals"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "residual reinforcement learning for robot control"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "residual policy learning"
    - "learning robotic assembly from cad"
    - "the limits and potentials of deep learning for robotics"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"

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)