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: "privacy preserving distributed deep learning for clinical data"
  Abstract: Deep learning with medical data often requires larger samples sizes than are available at single providers. While data sharing among institutions is desirable to train more accurate and sophisticated models, it can lead to severe privacy concerns due the sensitive nature of the data. This problem has motivated a number of studies on distributed training of neural networks that do not require direct sharing of the training data. However, simple distributed training does not offer provable privacy guarantees to satisfy technical safe standards and may reveal information about the underlying patients. We present a method to train neural networks for clinical data in a distributed fashion under differential privacy. We demonstrate these methods on two datasets that include information from multiple independent sites, the eICU collaborative Research Database and The Cancer Genome Atlas.

Connected through hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "reproducibility in machine learning for health"
    - "fairness in algorithmic decision making an excursion through the lens of causality"
    - "caveat emptor the risks of using big data for human development"
    - "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"
    - "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"
    - "self supervised domain adaptation for computer vision tasks"
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"

Connected through hyperedge HE-2 (induced by source paper "a federated learning framework for privacy preserving and parallel training"):
  with members:
    - "privacy preserving deep learning via weight transmission"
    - "towards fair and decentralized privacy preserving deep learning with blockchain"
    - "federated machine learning concept and applications"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "beyond data and model parallelism for deep neural networks"
    - "optimize deep convolutional neural network with ternarized weights and high accuracy"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "adversarial task specific privacy preservation under attribute attack"):
  with members:
    - "on learning invariant representation for domain adaptation"
    - "privacy partitioning protecting user data during the deep learning inference phase"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning adversarially fair and transferable representations"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "towards robust and privacy preserving text representations"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on fairness and calibration"

Connected through hyperedge HE-5 (induced by source paper "adversarial privacy preservation under attribute inference attack"):
  with members:
    - "on learning invariant representation for domain adaptation"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "age progression regression by conditional adversarial autoencoder"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "equality of opportunity in supervised learning"
    - "you are who you know and how you behave attribute inference attacks via users social friends and behaviors"
    - "wide residual networks"

Connected through hyperedge HE-6 (induced by source paper "differential privacy techniques for cyber physical systems a survey"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "sok differential privacies"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "achieving differential privacy against non intrusive load monitoring in smart grid a fog computing approach"
    - "toward practical privacy preserving analytics for iot and cloud based healthcare systems"
    - "re dpoctor real time health data releasing with w day differential privacy"
    - "charging and discharging of plug in electric vehicles pevs in vehicle to grid v2g systems a cyber insurance based model"

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)