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: "preserving differential privacy in convolutional deep belief networks"
  Abstract: The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions.

Connected through hyperedge HE-1 (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-2 (induced by source paper "preserving differential privacy in adversarial learning with provable robustness"):
  with members:
    - "privacy preserving collaborative deep learning with irregular participants"
    - "sok security and privacy in machine learning"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "extending defensive distillation"
    - "blocking transferability of adversarial examples in black box learning systems"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"

Connected through hyperedge HE-3 (induced by source paper "interpretable and differentially private predictions"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "smoothgrad removing noise by adding noise"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "machine learning models that remember too much"
    - "why should i trust you explaining the predictions of any classifier"
    - "privacy via the johnson lindenstrauss transform"

Connected through hyperedge HE-4 (induced by source paper "dp mac the differentially private method of auxiliary coordinates for deep learning"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "machine learning models that remember too much"
    - "distributed optimization of deeply nested systems"

Connected through hyperedge HE-5 (induced by source paper "differentially private generative adversarial network"):
  with members:
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "began boundary equilibrium generative adversarial networks"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "improved training of wasserstein gans"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "energy based generative adversarial network"

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)