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: "score function features for discriminative learning matrix and tensor framework"
  Abstract: Author(s): Janzamin, Majid; Sedghi, Hanie; Anandkumar, Anima | Abstract: Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms for extracting discriminative information, given these pre-trained features and labeled samples for any related task. Our class of features are based on higher-order score functions, which capture local variations in the probability density function of the input. We establish a theoretical framework to characterize the nature of discriminative information that can be extracted from score-function features, when used in conjunction with labeled samples. We employ efficient spectral decomposition algorithms (on matrices and tensors) for extracting discriminative components. The advantage of employing tensor-valued features is that we can extract richer discriminative information in the form of an overcomplete representations. Thus, we present a novel framework for employing generative models of the input for discriminative learning.

Connected through hyperedge HE-1 (induced by source paper "learning in gated neural networks"):
  with members:
    - "can sgd learn recurrent neural networks with provable generalization"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "robust spectral filtering and anomaly detection"
    - "towards binary valued gates for robust lstm training"
    - "learning one hidden layer neural networks under general input distributions"
    - "breaking the gridlock in mixture of experts consistent and efficient algorithms"
    - "on the convergence rate of training recurrent neural networks"
    - "learning without mixing towards a sharp analysis of linear system identification"

Connected through hyperedge HE-2 (induced by source paper "learning two layer rectified neural networks in polynomial time"):
  with members:
    - "complexity of training relu neural networks"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning one convolutional layer with overlapping patches"
    - "the computational complexity of training relu s"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"
    - "learning two layer neural networks with symmetric inputs"
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"

Connected through hyperedge HE-3 (induced by source paper "breaking the gridlock in mixture of experts consistent and efficient algorithms"):
  with members:
    - "deep mixture of experts via shallow embedding"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "learning one hidden layer neural networks with landscape design"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "attention is all you need"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"

Connected through hyperedge HE-4 (induced by source paper "beating the perils of non convexity guaranteed training of neural networks using tensor methods"):
  with members:
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "ell_1 regularized neural networks are improperly learnable in polynomial time"
    - "global optimality in tensor factorization deep learning and beyond"
    - "train faster generalize better stability of stochastic gradient descent"
    - "on the computational efficiency of training neural networks"
    - "provable methods for training neural networks with sparse connectivity"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"

Connected through hyperedge HE-5 (induced by source paper "training input output recurrent neural networks through spectral methods"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "reinforcement learning of pomdp s using spectral methods"
    - "strongly typed recurrent neural networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "nonparametric estimation of multi view latent variable models"
    - "what regularized auto encoders learn from the data generating distribution"

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"
    - "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"
    - "online tensor methods for learning latent variable models"

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)