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: "learning what data to learn"
  Abstract: Machine learning is essentially the sciences of playing with data. An adaptive data selection strategy, enabling to dynamically choose different data at various training stages, can reach a more effective model in a more efficient way. In this paper, we propose a deep reinforcement learning framework, which we call \emph{\textbf{N}eural \textbf{D}ata \textbf{F}ilter} (\textbf{NDF}), to explore automatic and adaptive data selection in the training process. In particular, NDF takes advantage of a deep neural network to adaptively select and filter important data instances from a sequential stream of training data, such that the future accumulative reward (e.g., the convergence speed) is maximized. In contrast to previous studies in data selection that is mainly based on heuristic strategies, NDF is quite generic and thus can be widely suitable for many machine learning tasks. Taking neural network training with stochastic gradient descent (SGD) as an example, comprehensive experiments with respect to various neural network modeling (e.g., multi-layer perceptron networks, convolutional neural networks and recurrent neural networks) and several applications (e.g., image classification and text understanding) demonstrate that NDF powered SGD can achieve comparable accuracy with standard SGD process by using less data and fewer iterations.

Connected through hyperedge HE-1 (induced by source paper "investigating the compositional structure of deep neural networks"):
  with members:
    - "deep neural network ensembles"
    - "not all samples are created equal deep learning with importance sampling"
    - "an empirical study of example forgetting during deep neural network learning"
    - "empirical bounds on linear regions of deep rectifier networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "understanding deep learning requires rethinking generalization"
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"

Connected through hyperedge HE-2 (induced by source paper "learning to selectively transfer reinforced transfer learning for deep text matching"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "reinforced co training"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "learning sampling policies for domain adaptation"
    - "reinforcement learning for relation classification from noisy data"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "learning how to active learn a deep reinforcement learning approach"

Connected through hyperedge HE-3 (induced by source paper "online deep reinforcement learning for autonomous uav navigation and exploration of outdoor environments"):
  with members:
    - "visual appearance analysis of forest scenes for monocular slam"
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "multi task regression based learning for autonomous unmanned aerial vehicle flight control within unstructured outdoor environments"
    - "transfer and online reinforcement learning in stt mram based embedded systems for autonomous drones"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "rainbow combining improvements in deep reinforcement learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-4 (induced by source paper "accelerating minibatch stochastic gradient descent using typicality sampling"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "importance sampling for minibatches"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "wavenet a generative model for raw audio"
    - "online batch selection for faster training of neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "skeleton based action recognition with synchronous local and non local spatio temporal learning and frequency attention"):
  with members:
    - "ensemble soft margin softmax loss for image classification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "non local recurrent network for image restoration"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "non local neural networks"
    - "focal loss for dense object detection"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "an empirical study of example forgetting during deep neural network learning"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "identifying generalization properties in neural networks"
    - "not all samples are created equal deep learning with importance sampling"
    - "an alternative view when does sgd escape local minima"
    - "on the learning dynamics of deep neural networks"
    - "improved regularization of convolutional neural networks with cutout"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-7 (induced by source paper "not all samples are created equal deep learning with importance sampling"):
  with members:
    - "sampling matters in deep embedding learning"
    - "accelerating deep neural network training with inconsistent stochastic gradient descent"
    - "wide residual networks"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "prioritized experience replay"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-8 (induced by source paper "exploiting class learnability in noisy data"):
  with members:
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "automated curriculum learning for neural networks"
    - "learning to select data for transfer learning with bayesian optimization"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "yfcc100m the new data in multimedia research"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "training convolutional networks with noisy labels"
    - "bandits and experts in metric spaces"

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)