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: "a concentration theorem for projections"
  Abstract: X in R^D has mean zero and finite second moments. We show that there is a precise sense in which almost all linear projections of X into R^d (for d < D) look like a scale-mixture of spherical Gaussians -- specifically, a mixture of distributions N(0, sigma^2 I_d) where the weight of the particular sigma component is P (| X |^2 = sigma^2 D). The extent of this effect depends upon the ratio of d to D, and upon a particular coefficient of eccentricity of X's distribution. We explore this result in a variety of experiments.

Connected through hyperedge HE-1 (induced by source paper "memory sample tradeoffs for linear regression with small error"):
  with members:
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "detecting correlations with little memory and communication"
    - "extractor based time space lower bounds for learning"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "on the complexity of bandit and derivative free stochastic convex optimization"

Connected through hyperedge HE-2 (induced by source paper "chance constrained ac optimal power flow a polynomial chaos approach"):
  with members:
    - "convex relaxations of chance constrained ac optimal power flow"

Connected through hyperedge HE-3 (induced by source paper "black box adversarial attacks with limited queries and information"):
  with members:
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "black box attacks against rnn based malware detection algorithms"
    - "on the limitation of convolutional neural networks in recognizing negative images"
    - "robust physical world attacks on machine learning models"
    - "simple black box adversarial perturbations for deep networks"
    - "synthesizing robust adversarial examples"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-4 (induced by source paper "query efficient black box adversarial examples superceded"):
  with members:
    - "synthesizing robust adversarial examples"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"
    - "approximation with random bases"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "linear time clustering for high dimensional mixtures of gaussian clouds"):
  with members:
    - "tight bounds for learning a mixture of two gaussians"
    - "a two round variant of em for gaussian mixtures"
    - "minimum complexity pursuit"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"

Connected through hyperedge HE-6 (induced by source paper "conditional central limit theorems for gaussian projections"):
  with members:
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation propagation for approximate bayesian inference"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "generalized entropy power inequalities and monotonicity properties of information"

Connected through hyperedge HE-7 (induced by source paper "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"):
  with members:
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "fast detection of overlapping communities via online tensor methods"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "smoothed analysis of tensor decompositions"
    - "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"
    - "nonparametric estimation of multi view latent variable models"
    - "fourier pca and robust tensor decomposition"
    - "understanding alternating minimization for matrix completion"

Connected through hyperedge HE-8 (induced by source paper "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"):
  with members:
    - "collaborative hierarchical sparse modeling"

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)