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: "designing neural network architectures using reinforcement learning"
  Abstract: At present, designing convolutional neural network (CNN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modified from a handful of existing networks. We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task. The learning agent is trained to sequentially choose CNN layers using $Q$-learning with an $\epsilon$-greedy exploration strategy and experience replay. The agent explores a large but finite space of possible architectures and iteratively discovers designs with improved performance on the learning task. On image classification benchmarks, the agent-designed networks (consisting of only standard convolution, pooling, and fully-connected layers) beat existing networks designed with the same layer types and are competitive against the state-of-the-art methods that use more complex layer types. We also outperform existing meta-modeling approaches for network design on image classification tasks.

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "dynamics aware unsupervised discovery of skills"
    - "causal confusion in imitation learning"
    - "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the conditional entropy bottleneck"

Connected through hyperedge HE-2 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "bats binary architecture search"):
  with members:
    - "learning architectures for binary networks"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "back to simplicity how to train accurate bnns from scratch"
    - "regularizing activation distribution for training binarized deep networks"
    - "accurate and compact convolutional neural networks with trained binarization"
    - "xnor net improved binary neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-4 (induced by source paper "nas count counting by density with neural architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-5 (induced by source paper "model agnostic structured sparsification with learnable channel shuffle"):
  with members:
    - "search to distill pearls are everywhere but not the eyes"
    - "differentiable learning to group channels via groupable convolutional neural networks"
    - "importance estimation for neural network pruning"
    - "fully learnable group convolution for acceleration of deep neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"

Connected through hyperedge HE-6 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation"):
  with members:
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "fast autoaugment"
    - "deep high resolution representation learning for human pose estimation"
    - "scale aware trident networks for object detection"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"
    - "regularized evolution for image classifier architecture search"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-7 (induced by source paper "sasl saliency adaptive sparsity learning for neural network acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "oicsr out in channel sparsity regularization for compact deep neural networks"
    - "importance estimation for neural network pruning"
    - "global sparse momentum sgd for pruning very deep neural networks"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "structured pruning of neural networks with budget aware regularization"

Connected through hyperedge HE-8 (induced by source paper "lightweight residual densely connected convolutional neural network"):
  with members:
    - "denser deep evolutionary network structured representation"
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "residual dense network for image restoration"
    - "convolutional neural networks with alternately updated clique"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "residual dense network for image super resolution"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

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)