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: "modeling documents with deep boltzmann machines"
  Abstract: We introduce a Deep Boltzmann Machine model suitable for modeling and extracting latent semantic representations from a large unstructured collection of documents. We overcome the apparent difficulty of training a DBM with judicious parameter tying. This parameter tying enables an efficient pretraining algorithm and a state initialization scheme that aids inference. The model can be trained just as efficiently as a standard Restricted Boltzmann Machine. Our experiments show that the model assigns better log probability to unseen data than the Replicated Softmax model. Features extracted from our model outperform LDA, Replicated Softmax, and DocNADE models on document retrieval and document classification tasks.

Connected through hyperedge HE-1 (induced by source paper "cooperative neural networks conn exploiting prior independence structure for improved classification"):
  with members:
    - "deep image super resolution via natural image priors"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "dynamic routing between capsules"
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "discriminative embeddings of latent variable models for structured data"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "neural variational inference for text processing"
    - "spatial transformer networks"

Connected through hyperedge HE-2 (induced by source paper "unfolding the structure of a document using deep learning"):
  with members:
    - "understanding and representing the semantics of large structured documents"
    - "understanding the logical and semantic structure of large documents"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "text understanding from scratch"
    - "generating news headlines with recurrent neural networks"
    - "convolutional neural networks for sentence classification"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "understanding and representing the semantics of large structured documents"):
  with members:
    - "a deep convolutional auto encoder with pooling unpooling layers in caffe"
    - "understanding the logical and semantic structure of large documents"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "generating news headlines with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "learning topics using semantic locality"):
  with members:
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "probabilistic latent semantic analysis"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "restricted boltzmann machines with gaussian visible units guided by pairwise constraints"):
  with members:
    - "gaussian binary restricted boltzmann machines for modeling natural image statistics"
    - "estimating 3d trajectories from 2d projections via disjunctive factored four way conditional restricted boltzmann machines"
    - "theta rbm unfactored gated restricted boltzmann machine for rotation invariant representations"
    - "a novel feature extraction method for scene recognition based on centered convolutional restricted boltzmann machines"
    - "deep transductive semi supervised maximum margin clustering"
    - "a generative restricted boltzmann machine based method for high dimensional motion data modeling"
    - "speech recognition with deep recurrent neural networks"
    - "conditional restricted boltzmann machines for structured output prediction"

Connected through hyperedge HE-6 (induced by source paper "piecewise latent variables for neural variational text processing"):
  with members:
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "improving variational inference with inverse autoregressive flow"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "smart reply automated response suggestion for email"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "generative deep neural networks for dialogue a short review"
    - "variational lossy autoencoder"
    - "document neural autoregressive distribution estimation"

Connected through hyperedge HE-7 (induced by source paper "document neural autoregressive distribution estimation"):
  with members:
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "improved semantic representations from tree structured long short term memory networks"):
  with members:
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "learning to execute"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"

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)