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: "a practical tutorial on autoencoders for nonlinear feature fusion taxonomy models software and guidelines"
  Abstract: Abstract   Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these variables is also important, since performance tends to decline as the input dimensionality increases, hence the interest in using feature fusion techniques, able to produce feature sets that are more compact and higher level. A plethora of procedures to fuse original variables for producing new ones has been developed in the past decades. The most basic ones use linear combinations of the original variables, such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), while others find manifold embeddings of lower dimensionality based on non-linear combinations, such as Isomap or LLE (Linear Locally Embedding) techniques.  More recently, autoencoders (AEs) have emerged as an alternative to manifold learning for conducting nonlinear feature fusion. Dozens of AE models have been proposed lately, each with its own specific traits. Although many of them can be used to generate reduced feature sets through the fusion of the original ones, there also AEs designed with other applications in mind.  The goal of this paper is to provide the reader with a broad view of what an AE is, how they are used for feature fusion, a taxonomy gathering a broad range of models, and how they relate to other classical techniques. In addition, a set of didactic guidelines on how to choose the proper AE for a given task is supplied, together with a discussion of the software tools available. Finally, two case studies illustrate the usage of AEs with datasets of handwritten digits and breast cancer.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on ensembles and deep learning fusion with mnist as guiding thread a complex heterogeneous fusion scheme reaching 10 digits error"):
  with members:
    - "path capsule networks"
    - "dynamic routing between capsules"
    - "recurrent neural network regularization"
    - "representation learning a review and new perspectives"
    - "multi column deep neural networks for image classification"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-2 (induced by source paper "multimodal deep network embedding with integrated structure and attribute information"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "attributed social network embedding"
    - "a survey on network embedding"
    - "mining application aware community organization with expanded feature subspaces from concerned attributes in social networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "blood lactate concentration prediction in critical care patients handling missing values"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "mida multiple imputation using denoising autoencoders"
    - "benchmark of deep learning models on large healthcare mimic datasets"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "recurrent neural networks for multivariate time series with missing values"

Connected through hyperedge HE-4 (induced by source paper "aegr a simple approach to gradient reversal in autoencoders for network anomaly detection"):
  with members:
    - "detection of anomalies in large scale accounting data using deep autoencoder networks"

Connected through hyperedge HE-5 (induced by source paper "data augmentation by autoencoders for unsupervised anomaly detection"):
  with members:
    - "doping generative data augmentation for unsupervised anomaly detection with gan"

Connected through hyperedge HE-6 (induced by source paper "ways of applying artificial intelligence in software engineering"):
  with members:
    - "automatic software repair a bibliography"
    - "deep learning a critical appraisal"
    - "the case for learned index structures"
    - "a learning algorithm for change impact prediction"
    - "building machines that learn and think like people"
    - "deep learning in neural networks"
    - "do system test cases grow old"

Connected through hyperedge HE-7 (induced by source paper "tips guidelines and tools for managing multi label datasets the mldr datasets r package and the cometa data repository"):
  with members:
    - "dealing with difficult minority labels in imbalanced mutilabel data sets"
    - "remedial hwr tackling multilabel imbalance through label decoupling and data resampling hybridization"
    - "hcp a flexible cnn framework for multi label image classification"
    - "deep learning for multi label classification"

Connected through hyperedge HE-8 (induced by source paper "aeknn an autoencoder knn based classifier with built in dimensionality reduction"):
  with members:
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "gated feedback recurrent neural networks"
    - "deep learning of representations looking forward"
    - "representation learning a review and new perspectives"

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)