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: "deep learning of part based representation of data using sparse autoencoders with nonnegativity constraints"
  Abstract: We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (nonnegativity-constrained autoencoder), that learns features that show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the algorithm is assessed based on decomposing data into parts and its prediction performance is tested on three standard image data sets and one text data set. The results indicate that the nonnegativity constraint forces the autoencoder to learn features that amount to a part-based representation of data, while improving sparsity and reconstruction quality in comparison with the traditional sparse autoencoder and nonnegative matrix factorization. It is also shown that this newly acquired representation improves the prediction performance of a deep neural network.

Connected through hyperedge HE-1 (induced by source paper "towards interpretable image synthesis by learning sparsely connected and or networks"):
  with members:
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "calibrating energy based generative adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "winner take all autoencoders"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-2 (induced by source paper "part based approximations for morphological operators using asymmetric auto encoders"):
  with members:
    - "deep learning of constrained autoencoders for enhanced understanding of data"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to generate chairs with convolutional neural networks"
    - "sparse modeling for image and vision processing"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-3 (induced by source paper "unsupervised deep transfer feature learning for medical image classification"):
  with members:
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "a variational autoencoder for probabilistic non negative matrix factorisation"):
  with members:
    - "minimum description length as an objective function for non negative matrix factorization"
    - "implicit reparameterization gradients"
    - "computing the kullback leibler divergence between two generalized gamma distributions"

Connected through hyperedge HE-5 (induced by source paper "deep learning of constrained autoencoders for enhanced understanding of data"):
  with members:
    - "importance weighted autoencoders"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-6 (induced by source paper "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"):
  with members:
    - "learning transferable features with deep adaptation networks"
    - "net2net accelerating learning via knowledge transfer"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "how transferable are features in deep neural networks"
    - "winner take all autoencoders"
    - "adadelta an adaptive learning rate method"
    - "theano new features and speed improvements"
    - "multidimensional classification of hippocampal shape features discriminates alzheimer s disease and mild cognitive impairment from normal aging"

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)