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: "deconvolutional paragraph representation learning"
  Abstract: Learning latent representations from long text sequences is an important first step in many natural language processing applications. Recurrent Neural Networks (RNNs) have become a cornerstone for this challenging task. However, the quality of sentences during RNN-based decoding (reconstruction) decreases with the length of the text. We propose a sequence-to-sequence, purely convolutional and deconvolutional autoencoding framework that is free of the above issue, while also being computationally efficient. The proposed method is simple, easy to implement and can be leveraged as a building block for many applications. We show empirically that compared to RNNs, our framework is better at reconstructing and correcting long paragraphs. Quantitative evaluation on semi-supervised text classification and summarization tasks demonstrate the potential for better utilization of long unlabeled text data.

Connected through hyperedge HE-1 (induced by source paper "domain adaptive text style transfer"):
  with members:
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "consistent dialogue generation with self supervised feature learning"
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"

Connected through hyperedge HE-2 (induced by source paper "how to fine tune bert for text classification"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "universal language model fine tuning for text classification"
    - "neural speed reading via skim rnn"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-3 (induced by source paper "encouraging paragraph embeddings to remember sentence identity improves classification"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-4 (induced by source paper "cyclical annealing schedule a simple approach to mitigating kl vanishing"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "stochastic wavenet a generative latent variable model for sequential data"
    - "isolating sources of disentanglement in variational autoencoders"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "latent intention dialogue models"
    - "snapshot ensembles train 1 get m for free"

Connected through hyperedge HE-5 (induced by source paper "structuring latent spaces for stylized response generation"):
  with members:
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "multi task deep neural networks for natural language understanding"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "consistent dialogue generation with self supervised feature learning"
    - "unsupervised text style transfer using language models as discriminators"
    - "the natural language decathlon multitask learning as question answering"
    - "the design and implementation of xiaoice an empathetic social chatbot"

Connected through hyperedge HE-6 (induced by source paper "multi view deep subspace clustering networks"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "deep multimodal subspace clustering networks"
    - "subspace clustering by block diagonal representation"
    - "simple to complex cross modal learning to rank"
    - "non local neural networks"
    - "deep subspace clustering networks"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "multi view 3d object detection network for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "hierarchical semantic correspondence learning for post discharge patient mortality prediction"):
  with members:
    - "joint slot filling and intent detection via capsule neural networks"
    - "patient representation learning and interpretable evaluation using clinical notes"
    - "deep contextualized word representations"
    - "deep relevance ranking using enhanced document query interactions"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "clinical concept extraction with contextual word embedding"
    - "scnet learning semantic correspondence"
    - "neural document embeddings for intensive care patient mortality prediction"

Connected through hyperedge HE-8 (induced by source paper "nash toward end to end neural architecture for generative semantic hashing"):
  with members:
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "deep supervised discrete hashing"
    - "topic compositional neural language model"
    - "deconvolutional latent variable model for text sequence matching"
    - "adaptive convolutional filter generation for natural language understanding"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "stochastic generative hashing"
    - "learning k way d dimensional discrete code for compact embedding representations"

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.CL(Computation and Language)