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 privately from multiparty data"
  Abstract: Learning a classifier from private data collected by multiple parties is an important problem that has many potential applications. How can we build an accurate and differentially private global classifier by combining locally-trained classifiers from different parties, without access to any party's private data? We propose to transfer the `knowledge' of the local classifier ensemble by first creating labeled data from auxiliary unlabeled data, and then train a global $\epsilon$-differentially private classifier. We show that majority voting is too sensitive and therefore propose a new risk weighted by class probabilities estimated from the ensemble. Relative to a non-private solution, our private solution has a generalization error bounded by $O(\epsilon^{-2}M^{-2})$ where $M$ is the number of parties. This allows strong privacy without performance loss when $M$ is large, such as in crowdsensing applications. We demonstrate the performance of our method with realistic tasks of activity recognition, network intrusion detection, and malicious URL detection.

Connected through hyperedge HE-1 (induced by source paper "differentially private deep learning with smooth sensitivity"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "learning differentially private recurrent language models"
    - "deep residual learning for image recognition"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "evaluating differentially private machine learning in practice"):
  with members:
    - "differentially private model publishing for deep learning"
    - "privacy preserving collaborative deep learning with irregular participants"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "dp admm admm based distributed learning with differential privacy"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "efficient deep learning on multi source private data"

Connected through hyperedge HE-3 (induced by source paper "reconciling utility and membership privacy via knowledge distillation"):
  with members:
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "evaluating differentially private machine learning in practice"
    - "zero shot knowledge distillation in deep networks"
    - "exploiting unintended feature leakage in collaborative learning"
    - "private model compression via knowledge distillation"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "i know what you see power side channel attack on convolutional neural network accelerators"

Connected through hyperedge HE-4 (induced by source paper "eavesdrop the composition proportion of training labels in federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "a little is enough circumventing defenses for distributed learning"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "federated learning for emoji prediction in a mobile keyboard"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "multi party poisoning through generalized p tampering"
    - "federated learning for ultra reliable low latency v2v communications"

Connected through hyperedge HE-5 (induced by source paper "key protected classification for collaborative learning"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "exploiting unintended feature leakage in collaborative learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "adversarial attacks and defences a survey"
    - "privacy preserving machine learning through data obfuscation"
    - "differentially private generative adversarial network"
    - "efficient deep learning on multi source private data"
    - "personalized and private peer to peer machine learning"

Connected through hyperedge HE-6 (induced by source paper "contamination attacks and mitigation in multi party machine learning"):
  with members:
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "an algorithmic framework for differentially private data analysis on trusted processors"
    - "is feature selection secure against training data poisoning"
    - "understanding membership inferences on well generalized learning models"
    - "logan membership inference attacks against generative models"
    - "learning differentially private recurrent language models"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "machine learning models that remember too much"

Connected through hyperedge HE-7 (induced by source paper "pac learning with stable and private predictions"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "privately answering classification queries in the agnostic pac model"
    - "towards demystifying membership inference attacks"
    - "model agnostic private learning via stability"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "towards measuring membership privacy"
    - "subgaussian tail bounds via stability arguments"

Connected through hyperedge HE-8 (induced by source paper "differentially private distributed data summarization under covariate shift"):
  with members:
    - "clustering algorithms for the centralized and local models"
    - "differentially private stochastic gradient descent for in rdbms analytics"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "communication efficient learning of deep networks from decentralized data"
    - "deep domain confusion maximizing for domain invariance"
    - "the composition theorem for differential privacy"
    - "a simple and practical algorithm for differentially private data release"
    - "differentially private empirical risk minimization"

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)