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: "topic compositional neural language model"
  Abstract: We propose a Topic Compositional Neural Language Model (TCNLM), a novel method designed to simultaneously capture both the global semantic meaning and the local word ordering structure in a document. The TCNLM learns the global semantic coherence of a document via a neural topic model, and the probability of each learned latent topic is further used to build a Mixture-of-Experts (MoE) language model, where each expert (corresponding to one topic) is a recurrent neural network (RNN) that accounts for learning the local structure of a word sequence. In order to train the MoE model efficiently, a matrix factorization method is applied, by extending each weight matrix of the RNN to be an ensemble of topic-dependent weight matrices. The degree to which each member of the ensemble is used is tied to the document-dependent probability of the corresponding topics. Experimental results on several corpora show that the proposed approach outperforms both a pure RNN-based model and other topic-guided language models. Further, our model yields sensible topics, and also has the capacity to generate meaningful sentences conditioned on given topics.

Connected through hyperedge HE-1 (induced by source paper "transformer xl attentive language models beyond a fixed length context"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mesh tensorflow deep learning for supercomputers"

Connected through hyperedge HE-2 (induced by source paper "an optimal transport framework for zero shot learning"):
  with members:
    - "improving textual network embedding with global attention via optimal transport"
    - "topic guided variational autoencoders for text generation"
    - "improving textual network learning with variational homophilic embeddings"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "discriminative learning of latent features for zero shot recognition"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "zero shot kernel learning"

Connected through hyperedge HE-3 (induced by source paper "topic guided variational autoencoders for text generation"):
  with members:
    - "sequence generation with guider network"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "texygen a benchmarking platform for text generation models"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "selective encoding for abstractive sentence summarization"

Connected through hyperedge HE-4 (induced by source paper "inset sentence infilling with inter sentential generative pre training"):
  with members:
    - "tigs an inference algorithm for text infilling with gradient search"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "linguistic versus latent relations for modeling coherent flow in paragraphs"
    - "the curious case of neural text degeneration"
    - "what makes a good story designing composite rewards for visual storytelling"
    - "spanbert improving pre training by representing and predicting spans"
    - "abductive reasoning as self supervision for common sense question answering"
    - "sentence bert sentence embeddings using siamese bert networks"

Connected through hyperedge HE-5 (induced by source paper "graph driven generative models for heterogeneous multi task learning"):
  with members:
    - "medgcn graph convolutional networks for multiple medical tasks"
    - "improving textual network learning with variational homophilic embeddings"
    - "explainable prediction of medical codes from clinical text"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "graph convolutional networks for text classification"
    - "joint embedding of words and labels for text classification"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"

Connected through hyperedge HE-6 (induced by source paper "improving textual network learning with variational homophilic embeddings"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "pytorch biggraph a large scale graph embedding system"
    - "network structure from rich but noisy data"
    - "understanding convolutional neural networks for text classification"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "interpretable vaes for nonlinear group factor analysis"
    - "nash toward end to end neural architecture for generative semantic hashing"

Connected through hyperedge HE-7 (induced by source paper "better long range dependency by bootstrapping a mutual information regularizer"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "mine mutual information neural estimation"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "adversarially regularized autoencoders"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-8 (induced by source paper "recurrent hierarchical topic guided neural language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "topic guided variational autoencoders for text generation"
    - "bottom up abstractive summarization"
    - "dirichlet belief networks for topic structure learning"
    - "attention is all you need"
    - "a neural knowledge language model"
    - "topically driven neural language model"
    - "discovering discrete latent topics with neural variational inference"

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)