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 unsupervised clustering using mixture of autoencoders"
  Abstract: Part of this work was done when Dejiao Zhang was doing an internship at Technicolor Research. Both Dejiao Zhang and Laura Balzano’s participations were funded by DARPA-16-43-D3M-FP-037. Both Yifan Sun and Brian Eriksson's participation occurred while also at Technicolor Research.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive approach to unsupervised embedding learning based on and algorithm"):
  with members:
    - "unsupervised deep learning by neighbourhood discovery"
    - "unsupervised embedding learning via invariant and spreading instance feature"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "invariant information clustering for unsupervised image classification and segmentation"
    - "representation learning by learning to count"
    - "temporal ensembling for semi supervised learning"
    - "split brain autoencoders unsupervised learning by cross channel prediction"

Connected through hyperedge HE-2 (induced by source paper "mixture of experts variational autoencoder for clustering and generating from similarity based representations"):
  with members:
    - "variational psom deep probabilistic clustering with self organizing maps"
    - "clustering with deep learning taxonomy and new methods"
    - "som vae interpretable discrete representation learning on time series"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-3 (induced by source paper "deep clustering based on a mixture of autoencoders"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "deep clustering for unsupervised learning of visual features"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "variational deep embedding an unsupervised and generative approach to clustering"

Connected through hyperedge HE-4 (induced by source paper "som vae interpretable discrete representation learning on time series"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "clustering meets implicit generative models"
    - "competitive training of mixtures of independent deep generative models"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "neural discrete representation learning"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "tensorflow a system for large scale machine learning"
    - "identifying similar patients using self organising maps a case study on type 1 diabetes self care survey responses"

Connected through hyperedge HE-5 (induced by source paper "segment based credit scoring using latent clusters in the variational autoencoder"):
  with members:
    - "deep feature consistent variational autoencoder"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "generating sentences from a continuous space"
    - "learning from synthetic data using a stacked multichannel autoencoder"
    - "issues challenges and tools of clustering algorithms"

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)