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 from data with heterogeneous noise using sgd"
  Abstract: We consider learning from data of variable quality that may be obtained from different heterogeneous sources. Addressing learning from heterogeneous data in its full generality is a challenging problem. In this paper, we adopt instead a model in which data is observed through heterogeneous noise, where the noise level reflects the quality of the data source. We study how to use stochastic gradient algorithms to learn in this model. Our study is motivated by two concrete examples where this problem arises naturally: learning with local differential privacy based on data from multiple sources with different privacy requirements, and learning from data with labels of variable quality. #R##N#The main contribution of this paper is to identify how heterogeneous noise impacts performance. We show that given two datasets with heterogeneous noise, the order in which to use them in standard SGD depends on the learning rate. We propose a method for changing the learning rate as a function of the heterogeneity, and prove new regret bounds for our method in two cases of interest. Experiments on real data show that our method performs better than using a single learning rate and using only the less noisy of the two datasets when the noise level is low to moderate.

Connected through hyperedge HE-1 (induced by source paper "gan leaks a taxonomy of membership inference attacks against gans"):
  with members:
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "non stationary texture synthesis by adversarial expansion"
    - "exploiting unintended feature leakage in collaborative learning"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "privacy protective gan for face de identification"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (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-3 (induced by source paper "parameter free online convex optimization with sub exponential noise"):
  with members:
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "coin betting and parameter free online learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "adaptive online learning"

Connected through hyperedge HE-4 (induced by source paper "parameter free locally differentially private stochastic subgradient descent"):
  with members:
    - "parameter free online convex optimization with sub exponential noise"
    - "black box reductions for parameter free online learning in banach spaces"
    - "training deep networks without learning rates through coin betting"
    - "scale free online learning"
    - "coin betting and parameter free online learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "no regret algorithms for unconstrained online convex optimization"

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"
    - "preserving differential privacy in convolutional deep belief networks"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-6 (induced by source paper "active learning from weak and strong labelers"):
  with members:
    - "distribution independent reliable learning"
    - "beyond disagreement based agnostic active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "agnostic active learning without constraints"

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)