human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "score function features for discriminative learning"):
  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.

Includes members:
  Member "sample complexity analysis for learning overcomplete latent variable models through tensor methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beating the perils of non convexity guaranteed training of neural networks using tensor methods")
  Member "provable methods for training neural networks with sparse connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on connected sublevel sets in deep learning")
  Member "provable tensor methods for learning mixtures of generalized linear models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta learning for mixed linear regression")
  Member "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis")
  Member "discriminative features via generalized eigenvectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis")
  Member "online tensor methods for learning latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "understanding generalization in deep learning via tensor methods")
  Member "representation learning a review and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nestedvae isolating common factors via weak supervision")

Question: Please tell me which class the source paper 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)