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 "maximum entropy linear manifold for learning discriminative low dimensional representation"):
  Abstract: Representation learning is currently a very hot topic in modern machine learning, mostly due to the great success of the deep learning methods. In particular low-dimensional representation which discriminates classes can not only enhance the classification procedure, but also make it faster, while contrary to the high-dimensional embeddings can be efficiently used for visual based exploratory data analysis. #R##N#In this paper we propose Maximum Entropy Linear Manifold (MELM), a multidimensional generalization of Multithreshold Entropy Linear Classifier model which is able to find a low-dimensional linear data projection maximizing discriminativeness of projected classes. As a result we obtain a linear embedding which can be used for classification, class aware dimensionality reduction and data visualization. MELM provides highly discriminative 2D projections of the data which can be used as a method for constructing robust classifiers. #R##N#We provide both empirical evaluation as well as some interesting theoretical properties of our objective function such us scale and affine transformation invariance, connections with PCA and bounding of the expected balanced accuracy error.

Includes members:
  Member "fast optimization of multithreshold entropy linear classifier":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multithreshold entropy linear classifier")
  Member "on the consistency of multithreshold entropy linear classifier":
    also connected through other hyperedges:
      (none)
  Member "multithreshold entropy linear classifier":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalized rbf kernel for incomplete data")
  Member "cross entropy clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient mixture model for clustering of sparse high dimensional binary data")
  Member "discriminative features via generalized eigenvectors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis")
  Member "on the local correctness of l 1 minimization for dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convergence radius and sample complexity of itkm algorithms for dictionary learning")
  Member "the numpy array a structure for efficient numerical computation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")

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)