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: "non negative matrix factorization with sparseness constraints"
  Abstract: Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating the notion of `sparseness' improves the found decompositions. Additionally, we provide complete MATLAB code both for standard NMF and for our extension. Our hope is that this will further the application of these methods to solving novel data-analysis problems.

Connected through hyperedge HE-1 (induced by source paper "disentangling overlapping beliefs by structured matrix factorization"):
  with members:
    - "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"
    - "users polarization on facebook and youtube"

Connected through hyperedge HE-2 (induced by source paper "deephoyer learning sparser neural network with differentiable scale invariant sparsity measures"):
  with members:
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "amc automl for model compression and acceleration on mobile devices"
    - "leveraging filter correlations for deep model compression"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "snip single shot network pruning based on connection sensitivity"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "cat compression aware training for bandwidth reduction"):
  with members:
    - "ebpc extended bit plane compression for deep neural network inference and training accelerators"
    - "compressibility loss for neural network weights"
    - "feature map transform coding for energy efficient cnn inference"
    - "quantization networks"
    - "lutnet rethinking inference in fpga soft logic"
    - "sparse networks from scratch faster training without losing performance"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "efficient and effective quantization for sparse dnns"

Connected through hyperedge HE-4 (induced by source paper "sparsity emerges naturally in neural language models"):
  with members:
    - "a deep generative model of vowel formant typology"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "understanding learning dynamics of language models with svcca"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "learning to generate reviews and discovering sentiment"
    - "exploring sparsity in recurrent neural networks"
    - "opening the black box of deep neural networks via information"
    - "spine sparse interpretable neural embeddings"

Connected through hyperedge HE-5 (induced by source paper "sparseout controlling sparsity in deep networks"):
  with members:
    - "bridgeout stochastic bridge regularization for deep neural networks"
    - "regularizing and optimizing lstm language models"
    - "sparse activity and sparse connectivity in supervised learning"
    - "wide residual networks"
    - "pointer sentinel mixture models"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "a critical review of recurrent neural networks for sequence learning"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-6 (induced by source paper "grouped sparse projection"):
  with members:
    - "deephoyer learning sparser neural network with differentiable scale invariant sparsity measures"
    - "learning sparse networks using targeted dropout"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "importance estimation for neural network pruning"
    - "the state of sparsity in deep neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "rethinking the value of network pruning"
    - "inductive bias of deep convolutional networks through pooling geometry"

Connected through hyperedge HE-7 (induced by source paper "unsupervised visual feature learning with spike timing dependent plasticity how far are we from traditional feature learning approaches"):
  with members:
    - "deep learning in spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "deep patch learning for weakly supervised object classification and discovery"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "training deep spiking neural networks using backpropagation"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "crf learning with cnn features for image segmentation"

Connected through hyperedge HE-8 (induced by source paper "one time learning in a biologically inspired salience affected artificial neural network sann"):
  with members:
    - "learning compact neural networks using ordinary differential equations as activation functions"
    - "an introduction to deep reinforcement learning"
    - "structured attention networks"
    - "attention is all you need"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "identity mappings in deep residual networks"
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"

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)