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: "distributed learning communication complexity and privacy"
  Abstract: We consider the problem of PAC-learning from distributed data and analyze fundamental communication complexity questions involved. We provide general upper and lower bounds on the amount of communication needed to learn well, showing that in addition to VC-dimension and covering number, quantities such as the teaching-dimension and mistake-bound of a class play an important role. We also present tight results for a number of common concept classes including conjunctions, parity functions, and decision lists. For linear separators, we show that for non-concentrated distributions, we can use a version of the Perceptron algorithm to learn with much less communication than the number of updates given by the usual margin bound. We also show how boosting can be performed in a generic manner in the distributed setting to achieve communication with only logarithmic dependence on 1/epsilon for any concept class, and demonstrate how recent work on agnostic learning from class-conditional queries can be used to achieve low communication in agnostic settings as well. We additionally present an analysis of privacy, considering both differential privacy and a notion of distributional privacy that is especially appealing in this context.

Connected through hyperedge HE-1 (induced by source paper "a survey on distributed machine learning"):
  with members:
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "how to backdoor federated learning"
    - "no peek a survey of private distributed deep learning"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "scaling deep learning on gpu and knights landing clusters"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "the communication complexity of optimization"):
  with members:
    - "distributed and streaming linear programming in low dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"
    - "distributed statistical estimation of matrix products with applications"
    - "l1 regression using lewis weights preconditioning and stochastic gradient descent"
    - "on communication complexity of classification problems"
    - "communication optimal distributed clustering"
    - "distributed low rank approximation of implicit functions of a matrix"

Connected through hyperedge HE-3 (induced by source paper "inference under information constraints ii communication constraints and shared randomness"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "test without trust optimal locally private distribution testing"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "sample optimal identity testing with high probability"
    - "a new approach for testing properties of discrete distributions"
    - "the optimality of correlated sampling"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"

Connected through hyperedge HE-4 (induced by source paper "model aggregation via good enough model spaces"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "multi institutional deep learning modeling without sharing patient data a feasibility study on brain tumor segmentation"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "overcoming catastrophic forgetting in neural networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "going digital a survey on digitalization and large scale data analytics in healthcare"
    - "communication complexity of distributed convex learning and optimization"

Connected through hyperedge HE-5 (induced by source paper "multi hop federated private data augmentation with sample compression"):
  with members:
    - "decentralized learning of generative adversarial networks from multi client non iid data"
    - "hybrid fl cooperative learning mechanism using non iid data in wireless networks"
    - "loadaboost loss based adaboost federated machine learning on medical data"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "zipnet gan inferring fine grained mobile traffic patterns via a generative adversarial neural network"
    - "rendergan generating realistic labeled data"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-6 (induced by source paper "pd ml lite private distributed machine learning from lighweight cryptography"):
  with members:
    - "a flexible privacy preserving framework for singular value decomposition under internet of things environment"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "typicality based stability and privacy"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "fast differentially private matrix factorization"
    - "a new approach to practical active secure two party computation"
    - "beyond worst case analysis in private singular vector computation"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-7 (induced by source paper "profile based privacy for locally private computations"):
  with members:
    - "the structure of optimal private tests for simple hypotheses"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "on the theory and practice of privacy preserving bayesian data analysis"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "geo indistinguishability differential privacy for location based systems"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-8 (induced by source paper "convex set disjointness distributed learning of halfspaces and lp feasibility"):
  with members:
    - "space lower bounds for linear prediction"
    - "the communication complexity of optimization"

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)