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: "the secret sharer measuring unintended neural network memorization extracting secrets"
  Abstract: Machine learning models based on neural networks and deep learning are being rapidly adopted for many purposes. What those models learn, and what they may share, is a significant concern when the training data may contain secrets and the models are public -- e.g., when a model helps users compose text messages using models trained on all users' messages. #R##N#This paper presents exposure: a simple-to-compute metric that can be applied to any deep learning model for measuring the memorization of secrets. Using this metric, we show how to extract those secrets efficiently using black-box API access. Further, we show that unintended memorization occurs early, is not due to over-fitting, and is a persistent issue across different types of models, hyperparameters, and training strategies. We experiment with both real-world models (e.g., a state-of-the-art translation model) and datasets (e.g., the Enron email dataset, which contains users' credit card numbers) to demonstrate both the utility of measuring exposure and the ability to extract secrets. #R##N#Finally, we consider many defenses, finding some ineffective (like regularization), and others to lack guarantees. However, by instantiating our own differentially-private recurrent model, we validate that by appropriately investing in the use of state-of-the-art techniques, the problem can be resolved, with high utility.

Connected through hyperedge HE-1 (induced by source paper "membership inference attacks on sequence to sequence models is my data in your machine translation system"):
  with members:
    - "openkiwi an open source framework for quality estimation"
    - "algorithms that remember model inversion attacks and data protection law"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"
    - "dual conditional cross entropy filtering of noisy parallel corpora"
    - "understanding membership inferences on well generalized learning models"
    - "the unintended consequences of overfitting training data inference attacks"
    - "knock knock who s there membership inference on aggregate location data"

Connected through hyperedge HE-2 (induced by source paper "tedl a text encryption method based on deep learning"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "lightweight efficient multi keyword ranked search over encrypted cloud data using dual word embeddings"
    - "recent trends in deep learning based natural language processing"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "membership inference attacks against machine learning models"
    - "towards biologically plausible deep learning"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "membership inference attacks and defenses in supervised learning via generalization gap"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"
    - "towards measuring membership privacy"
    - "logan membership inference attacks against generative models"
    - "mixup beyond empirical risk minimization"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "deep transfer learning with joint adaptation networks"

Connected through hyperedge HE-4 (induced by source paper "the differentially private lottery ticket mechanism"):
  with members:
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "differentially private generative adversarial network"
    - "learning differentially private recurrent language models"
    - "machine learning models that remember too much"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "gan leaks a taxonomy of membership inference attacks against gans"):
  with members:
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "non stationary texture synthesis by adversarial expansion"
    - "exploiting unintended feature leakage in collaborative learning"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "privacy protective gan for face de identification"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "differentially private generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "quantitative verification of neural networks and its security applications"):
  with members:
    - "phase transition behavior of cardinality and xor constraints"
    - "adversarial examples are a natural consequence of test error in noise"
    - "strip a defence against trojan attacks on deep neural networks"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial spheres"
    - "reachability analysis of deep neural networks with provable guarantees"

Connected through hyperedge HE-7 (induced by source paper "survey of attacks and defenses on edge deployed neural networks"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "bit flip attack crushing neural network with progressive bit search"
    - "xonn xnor based oblivious deep neural network inference"
    - "deeplaser practical fault attack on deep neural networks"
    - "knockoff nets stealing functionality of black box models"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"

Connected through hyperedge HE-8 (induced by source paper "generative models for effective ml on private decentralized datasets"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "taxonomy of real faults in deep learning systems"
    - "reconstruction and membership inference attacks against generative models"
    - "towards federated learning at scale system design"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "evaluating differentially private machine learning in practice"
    - "videoflow a flow based generative model for video"
    - "detecting bias with generative counterfactual face attribute augmentation"

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)