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: "to drop or not to drop robustness consistency and differential privacy properties of dropout"
  Abstract: Training deep belief networks (DBNs) requires optimizing a non-convex function with an extremely large number of parameters. Naturally, existing gradient descent (GD) based methods are prone to arbitrarily poor local minima. In this paper, we rigorously show that such local minima can be avoided (upto an approximation error) by using the dropout technique, a widely used heuristic in this domain. In particular, we show that by randomly dropping a few nodes of a one-hidden layer neural network, the training objective function, up to a certain approximation error, decreases by a multiplicative factor. #R##N#On the flip side, we show that for training convex empirical risk minimizers (ERM), dropout in fact acts as a "stabilizer" or regularizer. That is, a simple dropout based GD method for convex ERMs is stable in the face of arbitrary changes to any one of the training points. Using the above assertion, we show that dropout provides fast rates for generalization error in learning (convex) generalized linear models (GLM). Moreover, using the above mentioned stability properties of dropout, we design dropout based differentially private algorithms for solving ERMs. The learned GLM thus, preserves privacy of each of the individual training points while providing accurate predictions for new test points. Finally, we empirically validate our stability assertions for dropout in the context of convex ERMs and show that surprisingly, dropout significantly outperforms (in terms of prediction accuracy) the L2 regularization based methods for several benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "towards automatic generation of shareable synthetic clinical notes using neural language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep ehr chronic disease prediction using medical notes"
    - "towards the creation of a large corpus of synthetically identified clinical notes"
    - "fine tuned language models for text classification"
    - "lessons from natural language inference in the clinical domain"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "towards measuring membership privacy"
    - "privacy risk in machine learning analyzing the connection to overfitting"

Connected through hyperedge HE-2 (induced by source paper "aggregating votes with local differential privacy usefulness soundness vs indistinguishability"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "tight lower bounds for locally differentially private selection"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "tight lower bounds for differentially private selection"
    - "marginal release under local differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "collecting telemetry data privately"
    - "collecting and analyzing data from smart device users with local differential privacy"

Connected through hyperedge HE-3 (induced by source paper "privacy preserving deep learning via weight transmission"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "gazelle a low latency framework for secure neural network inference"
    - "mixup beyond empirical risk minimization"
    - "deepsecure scalable provably secure deep learning"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "identity mappings in deep residual networks"
    - "stealing machine learning models via prediction apis"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"

Connected through hyperedge HE-4 (induced by source paper "deep models under the gan information leakage from collaborative deep learning"):
  with members:
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "invertible conditional gans for image editing"
    - "advanced probabilistic couplings for differential privacy"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "defeating image obfuscation with deep learning"
    - "improved techniques for training gans"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "regularizing deep neural networks by noise its interpretation and optimization"):
  with members:
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "improved dropout for shallow and deep learning"
    - "dropout with expectation linear regularization"
    - "convolutional two stream network fusion for video action recognition"
    - "variational inference for monte carlo objectives"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"

Connected through hyperedge HE-6 (induced by source paper "adversarial dropout for supervised and semi supervised learning"):
  with members:
    - "identity mappings in deep residual networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"
    - "densely connected convolutional networks"
    - "improved dropout for shallow and deep learning"
    - "adversarial examples in the physical world"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "ensemble robustness of deep learning algorithms"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "prioritized experience replay"
    - "train faster generalize better stability of stochastic gradient descent"
    - "end to end training of deep visuomotor policies"
    - "the loss surfaces of multilayer networks"
    - "towards deep neural network architectures robust to adversarial examples"
    - "intriguing properties of neural networks"
    - "robustness and generalization"

Connected through hyperedge HE-8 (induced by source paper "dropout with expectation linear regularization"):
  with members:
    - "surprising properties of dropout in deep networks"
    - "deep learning in neural networks"
    - "striving for simplicity the all convolutional net"
    - "on the inductive bias of dropout"
    - "dropout rademacher complexity of deep neural networks"
    - "dropout training for support vector machines"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)