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: "differentially private mixture of generative neural networks"
  Abstract: Generative models are used in a wide range of applications building on large amounts of contextually rich information. Due to possible privacy violations of the individuals whose data is used to train these models, however, publishing or sharing generative models is not always viable. In this paper, we present a novel technique for privately releasing generative models and entire high-dimensional datasets produced by these models. We model the generator distribution of the training data with a mixture of $k$ generative neural networks. These are trained together and collectively learn the generator distribution of a dataset. Data is divided into $k$ clusters, using a novel differentially private kernel $k$-means, then each cluster is given to separate generative neural networks, such as Restricted Boltzmann Machines or Variational Autoencoders, which are trained only on their own cluster using differentially private gradient descent. We evaluate our approach using the MNIST dataset, as well as call detail records and transit datasets, showing that it produces realistic synthetic samples, which can also be used to accurately compute arbitrary number of counting queries.

Connected through hyperedge HE-1 (induced by source paper "techniques d anonymisation tabulaire concepts et mise en oeuvre"):
  with members:
    - "when the signal is in the noise exploiting diffix s sticky noise"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "from t closeness to differential privacy and vice versa in data anonymization"
    - "publishing microdata with a robust privacy guarantee"

Connected through hyperedge HE-2 (induced by source paper "differentially private mixed type data generation for unsupervised learning"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "differentially private learning with adaptive clipping"
    - "differentially private model publishing for deep learning"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "private selection from private candidates"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"
    - "generating artificial data for private deep learning"

Connected through hyperedge HE-3 (induced by source paper "locally differentially private data collection and analysis"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "linear queries estimation with local differential privacy"
    - "locally private gaussian estimation"
    - "locally private mean estimation z test and tight confidence intervals"
    - "locally differentially private heavy hitter identification"
    - "renyi differential privacy"
    - "heavy hitters and the structure of local privacy"

Connected through hyperedge HE-4 (induced by source paper "challenges of privacy preserving machine learning in iot"):
  with members:
    - "on lightweight privacy preserving collaborative learning for internet of things objects"
    - "analyzing federated learning through an adversarial lens"
    - "how to backdoor federated learning"
    - "context aware generative adversarial privacy"
    - "privacy preserving personal model training"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "improving the robustness of deep neural networks via stability training"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving gradient boosting decision trees"):
  with members:
    - "a survey on federated learning systems vision hype and reality for data privacy and protection"
    - "practical federated gradient boosting decision trees"
    - "multi layered gradient boosting decision trees"
    - "towards demystifying membership inference attacks"
    - "catboost unbiased boosting with categorical features"
    - "xgboost a scalable tree boosting system"
    - "membership inference attacks against machine learning models"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-6 (induced by source paper "differentially private data generative models"):
  with members:
    - "performing co membership attacks against deep generative models"
    - "knock knock who s there membership inference on aggregate location data"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "learning privately from multiparty data"
    - "differentially private publication of location entropy"
    - "membership inference attacks against machine learning models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "towards vision based deep reinforcement learning for robotic motion control"

Connected through hyperedge HE-7 (induced by source paper "relations among different privacy notions"):
  with members:
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "preserving privacy enables coexistence equilibrium of competitive diffusion in social networks"
    - "composition properties of inferential privacy for time series data"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "pufferfish privacy mechanisms for correlated data"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"

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)