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: "least squares revisited scalable approaches for multi class prediction"
  Abstract: This work provides simple algorithms for multi-class (and multi-label) prediction in settings where both the number of examples n and the data dimension d are relatively large. These robust and parameter free algorithms are essentially iterative least-squares updates and very versatile both in theory and in practice. On the theoretical front, we present several variants with convergence guarantees. Owing to their effective use of second-order structure, these algorithms are substantially better than first-order methods in many practical scenarios. On the empirical side, we present a scalable stagewise variant of our approach, which achieves dramatic computational speedups over popular optimization packages such as Liblinear and Vowpal Wabbit on standard datasets (MNIST and CIFAR-10), while attaining state-of-the-art accuracies.

Connected through hyperedge HE-1 (induced by source paper "efficient hyperparameter optimization and infinitely many armed bandits"):
  with members:
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "a stratified analysis of bayesian optimization methods"
    - "thoughts on massively scalable gaussian processes"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "sharp analysis of low rank kernel matrix approximations"
    - "fast cross validation via sequential testing"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-2 (induced by source paper "hyperband a novel bandit based approach to hyperparameter optimization"):
  with members:
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "thoughts on massively scalable gaussian processes"
    - "simple regret for infinitely many armed bandits"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "convolutional neural networks applied to house numbers digit classification"
    - "fast cross validation via sequential testing"
    - "efficient multi start strategies for local search algorithms"

Connected through hyperedge HE-3 (induced by source paper "large scale kernel learning using block coordinate descent"):
  with members:
    - "adding vs averaging in distributed primal dual optimization"
    - "communication efficient distributed dual coordinate ascent"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "sharp analysis of low rank kernel matrix approximations"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-4 (induced by source paper "learning reductions that really work"):
  with members:
    - "learning to search better than your teacher"
    - "logarithmic time online multiclass prediction"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "efficient programmable learning to search"
    - "normalized online learning"
    - "surrogate regret bounds for bipartite ranking via strongly proper losses"
    - "output space search for structured prediction"
    - "online importance weight aware updates"

Connected through hyperedge HE-5 (induced by source paper "scalable multilabel prediction via randomized methods"):
  with members:
    - "fast label embeddings for extremely large output spaces"
    - "comparing published multi label classifier performance measures to the ones obtained by a simple multi label baseline classifier"
    - "logarithmic time online multiclass prediction"
    - "efficient exact gradient update for training deep networks with very large sparse targets"
    - "fastfood approximate kernel expansions in loglinear time"
    - "simple and deterministic matrix sketching"
    - "weak calibration is computationally hard"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "provable tensor methods for learning mixtures of generalized linear models"):
  with members:
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "learning mixtures of ranking models"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "score function features for discriminative learning matrix and tensor framework"
    - "fast detection of overlapping communities via online tensor methods"
    - "discriminative features via generalized eigenvectors"
    - "learning mixtures of linear classifiers"
    - "nonparametric estimation of multi view latent variable models"

Connected through hyperedge HE-7 (induced by source paper "fast label embeddings via randomized linear algebra"):
  with members:
    - "logarithmic time online multiclass prediction"
    - "zero shot learning through cross modal transfer"
    - "efficient estimation of word representations in vector space"
    - "word emdeddings through hellinger pca"
    - "simple and deterministic matrix sketching"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "scalable kernel methods via doubly stochastic gradients"):
  with members:
    - "on the equivalence between quadrature rules and random features"
    - "on data preconditioning for regularized loss minimization"
    - "deep fried convnets"
    - "random feature maps for dot product kernels"
    - "making gradient descent optimal for strongly convex stochastic 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)