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: "crypto nets neural networks over encrypted data"
  Abstract: The problem we address is the following: how can a user employ a predictive model that is held by a third party, without compromising private information. For example, a hospital may wish to use a cloud service to predict the readmission risk of a patient. However, due to regulations, the patient's medical files cannot be revealed. The goal is to make an inference using the model, without jeopardizing the accuracy of the prediction or the privacy of the data. #R##N#To achieve high accuracy, we use neural networks, which have been shown to outperform other learning models for many tasks. To achieve the privacy requirements, we use homomorphic encryption in the following protocol: the data owner encrypts the data and sends the ciphertexts to the third party to obtain a prediction from a trained model. The model operates on these ciphertexts and sends back the encrypted prediction. In this protocol, not only the data remains private, even the values predicted are available only to the data owner. #R##N#Using homomorphic encryption and modifications to the activation functions and training algorithms of neural networks, we show that it is protocol is possible and may be feasible. This method paves the way to build a secure cloud-based neural network prediction services without invading users' privacy.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "k beam minimax efficient optimization for deep adversarial learning"
    - "image to image translation for cross domain disentanglement"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "adversarial image perturbation for privacy protection a game theory perspective"

Connected through hyperedge HE-2 (induced by source paper "towards fair and decentralized privacy preserving deep learning with blockchain"):
  with members:
    - "differentially private releasing via deep generative model technical report"
    - "modelchain decentralized privacy preserving healthcare predictive modeling framework on private blockchain networks"
    - "dimensionality driven learning with noisy labels"
    - "iterative learning with open set noisy labels"
    - "differentially private fair learning"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-3 (induced by source paper "towards fair and decentralized privacy preserving deep learning"):
  with members:
    - "modelchain decentralized privacy preserving healthcare predictive modeling framework on private blockchain networks"
    - "dimensionality driven learning with noisy labels"
    - "iterative learning with open set noisy labels"
    - "differentially private fair learning"
    - "learning differentially private recurrent language models"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-4 (induced by source paper "using fuzzy bits and neural networks to partially invert few rounds of some cryptographic hash functions"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "one way hash function based on neural network"

Connected through hyperedge HE-5 (induced by source paper "disguised nets image disguising for privacy preserving outsourced deep learning"):
  with members:
    - "membership inference attacks against machine learning models"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "star statistical tests with auditable results"):
  with members:
    - "controlling false discoveries during interactive data exploration"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-7 (induced by source paper "custodes auditable hypothesis testing"):
  with members:
    - "controlling false discoveries during interactive data exploration"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-8 (induced by source paper "panda facilitating usable ai development"):
  with members:
    - "rafiki machine learning as an analytics service system"
    - "medical concept embedding with time aware attention"
    - "deep forest"
    - "forkbase an efficient storage engine for blockchain and forkable applications"
    - "progressive neural architecture search"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "the case for learned index structures"
    - "thoracic disease identification and localization with limited supervision"

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)