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 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 methods, including first-order iterative methods and polynomial-time 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#A new technical ingredient of our work is SQ algorithms for estimating the mean vector of a distribution over vectors in $\mathbb{R}^d$ with optimal estimation complexity. This is a natural problem 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 "recent advances in algorithmic high dimensional robust statistics"):
  with members:
    - "faster algorithms for high dimensional robust covariance estimation"
    - "list decodable linear regression"
    - "high dimensional robust estimation of sparse models via trimmed hard thresholding"
    - "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries"
    - "how hard is robust mean estimation"
    - "list decodable learning via sum of squares"
    - "quantum entropy scoring for fast robust mean estimation and improved outlier detection"
    - "high dimensional estimation via sum of squares proofs"

Connected through hyperedge HE-2 (induced by source paper "time accuracy tradeoffs for learning a relu with respect to gaussian marginals"):
  with members:
    - "complexity of training relu neural networks"
    - "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"
    - "efficient active learning of sparse halfspaces"
    - "learning one convolutional layer with overlapping patches"
    - "the computational complexity of training relu s"
    - "learning two layer neural networks with symmetric inputs"
    - "learning one hidden layer neural networks with landscape design"
    - "learning geometric concepts with nasty noise"

Connected through hyperedge HE-3 (induced by source paper "efficient algorithms and lower bounds for robust linear regression"):
  with members:
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "outlier robust moment estimation via sum of squares"
    - "being robust in high dimensions can be practical"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "learning from untrusted data"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"

Connected through hyperedge HE-4 (induced by source paper "failures of gradient based deep learning"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "distribution specific hardness of learning neural networks"
    - "on the sample complexity of end to end training vs semantic abstraction training"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "trust region policy optimization"

Connected through hyperedge HE-5 (induced by source paper "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"):
  with members:
    - "being robust in high dimensions can be practical"
    - "robust sparse estimation tasks in high dimensions"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "a size free clt for poisson multinomials and its applications"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "a general characterization of the statistical query complexity"
    - "beyond spectral tight bounds for planted gaussians"

Connected through hyperedge HE-6 (induced by source paper "distribution specific hardness of learning neural networks"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "learning halfspaces and neural networks with random initialization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "beyond convexity stochastic quasi convex optimization"
    - "on the quality of the initial basin in overspecified neural networks"
    - "on the computational efficiency of training neural networks"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-7 (induced by source paper "on the complexity of random satisfiability problems with planted solutions"):
  with members:
    - "how to refute a random csp"
    - "subsampled power iteration a unified algorithm for block models and planted csp s"
    - "human computable passwords"
    - "spectral redemption in clustering sparse networks"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "community detection thresholds and the weak ramanujan property"
    - "a complete characterization of statistical query learning with applications to evolvability"
    - "approximation resistant predicates from pairwise independence"

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)