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: "statistical query algorithms for mean vector estimation and stochastic convex optimization"
  Abstract: Stochastic convex optimization, where the objective is the expectation of a random convex function, is an important and widely used method with numerous applications in machine learning, statistics, operations research and other areas. We study the complexity of stochastic convex optimization given only statistical query (SQ) access to the objective function. We show that well-known and popular first-order iterative methods can be implemented using only statistical queries. For many cases of interest we derive nearly matching upper and lower bounds on the estimation (sample) complexity including linear optimization in the most general setting. We then present several consequences for machine learning, differential privacy and proving concrete lower bounds on the power of convex optimization based methods. #R##N#The key ingredient of our work is SQ algorithms and lower bounds for estimating the mean vector of a distribution over vectors supported on a convex body in $\mathbb{R}^d$. This natural problem has not been previously studied and we show that our solutions can be used to get substantially improved SQ versions of Perceptron and other online algorithms for learning halfspaces.

Connected through hyperedge HE-1 (induced by source paper "limits on gradient compression for stochastic optimization"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "nuqsgd improved communication efficiency for data parallel sgd via nonuniform quantization"
    - "vqsgd vector quantized stochastic gradient descent"
    - "distributed learning with sublinear communication"
    - "finite precision stochastic optimization accounting for the bias"
    - "ratq a universal fixed length quantizer for stochastic optimization"
    - "sparsified sgd with memory"
    - "inference under information constraints i lower bounds from chi square contraction"

Connected through hyperedge HE-2 (induced by source paper "poly time universality and limitations of deep learning"):
  with members:
    - "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"
    - "provable limitations of deep learning"
    - "on the complexity of learning neural networks"
    - "extractor based time space lower bounds for learning"
    - "time space tradeoffs for learning from small test spaces learning low degree polynomial functions"
    - "understanding deep learning requires rethinking generalization"
    - "distribution specific hardness of learning neural networks"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-3 (induced by source paper "interaction is necessary for distributed learning with privacy or communication constraints"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "distributed learning with sublinear communication"
    - "locally private learning without interaction requires separation"
    - "estimating smooth glm in non interactive local differential privacy model with public unlabeled data"
    - "exponential separations in local differential privacy through communication complexity"
    - "the role of interactivity in local differential privacy"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "learning without interaction requires separation"

Connected through hyperedge HE-4 (induced by source paper "private learning implies online learning an efficient reduction"):
  with members:
    - "passing tests without memorizing two models for fooling discriminators"
    - "efficient private algorithms for learning halfspaces"
    - "private pac learning implies finite littlestone dimension"
    - "learning in non convex games with an optimization oracle"
    - "how to use heuristics for differential privacy"
    - "calibrating noise to variance in adaptive data analysis"
    - "oracle efficient online learning and auction design"
    - "differentially private release and learning of threshold functions"

Connected through hyperedge HE-5 (induced by source paper "efficient symmetric norm regression via linear sketching"):
  with members:
    - "on mean estimation for general norms with statistical queries"
    - "dimensionality reduction for tukey regression"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "subspace embedding and linear regression with orlicz norm"
    - "algorithmic theory of odes and sampling from well conditioned logconcave densities"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "approximate near neighbors for general symmetric norms"
    - "input sparsity and hardness for robust subspace approximation"

Connected through hyperedge HE-6 (induced by source paper "locally private learning without interaction requires separation"):
  with members:
    - "exponential separations in local differential privacy"
    - "the role of interactivity in local differential privacy"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "collecting telemetry data privately"
    - "distributed mean estimation with limited communication"
    - "the limitations of optimization from samples"
    - "algorithmic stability for adaptive data analysis"

Connected through hyperedge HE-7 (induced by source paper "private stochastic convex optimization with optimal rates"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "preserving statistical validity in adaptive data analysis"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"

Connected through hyperedge HE-8 (induced by source paper "on mean estimation for general norms with statistical queries"):
  with members:
    - "efficient algorithms and lower bounds for robust linear regression"
    - "towards a zero one law for entrywise low rank approximation"
    - "subspace embedding and linear regression with orlicz norm"
    - "on the complexity of learning neural networks"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "approximate near neighbors for general symmetric norms"
    - "a general characterization of the statistical query complexity"
    - "streaming symmetric norms via measure concentration"

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)