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 unintended consequences of overfitting training data inference attacks"
  Abstract: Machine learning algorithms that are applied to sensitive data pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak specific private information in the training data to an attacker, either through their structure or their observable behavior. However, the underlying cause of this privacy risk is not well understood beyond a handful of anecdotal accounts that suggest overfitting and influence might play a role. #R##N#This paper examines the effect that overfitting and influence have on the ability of an attacker to learn information about training data from machine learning models, either through training set membership inference or model inversion attacks. Using both formal and empirical analyses, we illustrate a clear relationship between these factors and the privacy risk that arises in several popular machine learning algorithms. We find that overfitting is sufficient to allow an attacker to perform membership inference, and when certain conditions on the influence of certain features are present, model inversion attacks. Interestingly, our formal analysis also shows that overfitting is not necessary for these attacks, and begins to shed light on what other factors may be in play. Finally, we explore the connection between two types of attack, membership inference and model inversion, and show that there are deep connections between the two that lead to effective new attacks.

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"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "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"
    - "knock knock who s there membership inference on aggregate location data"

Connected through hyperedge HE-2 (induced by source paper "reconstruction and membership inference attacks against generative models"):
  with members:
    - "generative poisoning attack method against neural networks"
    - "logan membership inference attacks against generative models"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"
    - "on the quantitative analysis of decoder based generative models"

Connected through hyperedge HE-3 (induced by source paper "performing co membership attacks against deep generative models"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "learning to protect communications with adversarial neural cryptography"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "logan membership inference attacks against generative models"
    - "towards deeper understanding of variational autoencoding models"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-4 (induced by source paper "algorithms that remember model inversion attacks and data protection law"):
  with members:
    - "decision provenance harnessing data flow for accountable systems"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "third party tracking in the mobile ecosystem"
    - "speaker independent machine lip reading with speaker dependent viseme classifiers"
    - "knock knock who s there membership inference on aggregate location data"
    - "machine learning models that remember too much"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"

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.CR(Cryptography and Security)