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: "discriminative features via generalized eigenvectors"
  Abstract: Representing examples in a way that is compatible with the underlying classifier can greatly enhance the performance of a learning system. In this paper we investigate scalable techniques for inducing discriminative features by taking advantage of simple second order structure in the data. We focus on multiclass classification and show that features extracted from the generalized eigenvectors of the class conditional second moments lead to classifiers with excellent empirical performance. Moreover, these features have attractive theoretical properties, such as inducing representations that are invariant to linear transformations of the input. We evaluate classifiers built from these features on three different tasks, obtaining state of the art results.

Connected through hyperedge HE-1 (induced by source paper "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"):
  with members:
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "large scale approximate kernel canonical correlation analysis"
    - "nonparametric canonical correlation analysis"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "efficient dimensionality reduction for canonical correlation analysis"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-2 (induced by source paper "maximum entropy linear manifold for learning discriminative low dimensional representation"):
  with members:
    - "fast optimization of multithreshold entropy linear classifier"
    - "on the consistency of multithreshold entropy linear classifier"
    - "multithreshold entropy linear classifier"
    - "cross entropy clustering"
    - "on the local correctness of l 1 minimization for dictionary learning"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-3 (induced by source paper "score function features for discriminative learning matrix and tensor framework"):
  with members:
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "how transferable are features in deep neural networks"
    - "provable tensor methods for learning mixtures of classifiers"
    - "provable methods for training neural networks with sparse connectivity"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "provable bounds for learning some deep representations"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-4 (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"
    - "learning mixtures of linear classifiers"
    - "nonparametric estimation of multi view latent variable models"
    - "online tensor methods for learning latent variable models"

Connected through hyperedge HE-5 (induced by source paper "score function features for discriminative learning"):
  with members:
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "provable methods for training neural networks with sparse connectivity"
    - "provable tensor methods for learning mixtures of generalized linear models"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "online tensor methods for learning latent variable models"
    - "representation learning a review and new perspectives"

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)