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: "algorithms that remember model inversion attacks and data protection law"
  Abstract: Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms. The EU9s recent General Data Protection Regulation (GDPR) has been seen as a core tool for achieving better governance of this area. While the GDPR does apply to the use of models in some limited situations, most of its provisions relate to the governance of personal data, while models have traditionally been seen as intellectual property. We present recent work from the information security literature around ‘model inversion’ and ‘membership inference’ attacks, which indicates that the process of turning training data into machine-learned systems is not one way, and demonstrate how this could lead some models to be legally classified as personal data. Taking this as a probing experiment, we explore the different rights and obligations this would trigger and their utility, and posit future directions for algorithmic governance and regulation.  This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.

Connected through hyperedge HE-1 (induced by source paper "bias in data driven ai systems an introductory survey"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "explaining explanations in ai"
    - "adafair cumulative fairness adaptive boosting"
    - "from soft classifiers to hard decisions how fair can we be"
    - "a gradient based split criterion for highly accurate and transparent model trees"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fair clustering through fairlets"
    - "methods for interpreting and understanding deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "data and model dependencies of membership inference attack"):
  with members:
    - "gan leaks a taxonomy of membership inference attacks against gans"
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "membership inference attacks on sequence to sequence models"
    - "towards demystifying membership inference attacks"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "marketplace for ai models"):
  with members:
    - "bias in data driven ai systems an introductory survey"
    - "trustworthy ai in the age of pervasive computing and big data"
    - "transfer learning with dynamic distribution adaptation"
    - "the non iid data quagmire of decentralized machine learning"
    - "free riders in federated learning attacks and defenses"
    - "analyzing federated learning through an adversarial lens"
    - "federated learning strategies for improving communication efficiency"
    - "federated learning for mobile keyboard prediction"

Connected through hyperedge HE-5 (induced by source paper "formalizing data deletion in the context of the right to be forgotten"):
  with members:
    - "machine unlearning linear filtration for logit based classifiers"
    - "eternal sunshine of the spotless net selective forgetting in deep networks"
    - "making ai forget you data deletion in machine learning"
    - "proving erasure"
    - "machine unlearning"
    - "machine learning models that remember too much"

Connected through hyperedge HE-6 (induced by source paper "monitoring misuse for accountable artificial intelligence as a service"):
  with members:
    - "decision provenance harnessing data flow for accountable systems"
    - "artificial intelligence as a services ai aas on software defined infrastructure"
    - "responsible facial recognition and beyond"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "stealing machine learning models via prediction apis"
    - "societal economic ethical and legal challenges of the digital revolution from big data to deep learning artificial intelligence and manipulative technologies"

Connected through hyperedge HE-7 (induced by source paper "machine unlearning linear filtration for logit based classifiers"):
  with members:
    - "explainable artificial intelligence and machine learning a reality rooted perspective"
    - "logan membership inference attacks against generative models"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "membership inference attacks against machine learning models"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "model cards for model reporting"
    - "a framework for understanding unintended consequences of machine learning"
    - "spectrum data poisoning with adversarial deep learning"
    - "explaining explanations in ai"
    - "problem formulation and fairness"

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)