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: "learning neural trans dimensional random field language models with noise contrastive estimation"
  Abstract: Trans-dimensional random field language models (TRF LMs) where sentences are modeled as a collection of random fields, have shown close performance with LSTM LMs in speech recognition and are computationally more efficient in inference. However, the training efficiency of neural TRF LMs is not satisfactory, which limits the scalability of TRF LMs on large training corpus. In this paper, several techniques on both model formulation and parameter estimation are proposed to improve the training efficiency and the performance of neural TRF LMs. First, TRFs are reformulated in the form of exponential tilting of a reference distribution. Second, noise-contrastive estimation (NCE) is introduced to jointly estimate the model parameters and normalization constants. Third, we extend the neural TRF LMs by marrying the deep convolutional neural network (CNN) and the bidirectional LSTM into the potential function to extract the deep hierarchical features and bidirectionally sequential features. Utilizing all the above techniques enables the successful and efficient training of neural TRF LMs on a 40x larger training set with only 1/3 training time and further reduces the WER with relative reduction of 4.7% on top of a strong LSTM LM baseline.

Connected through hyperedge HE-1 (induced by source paper "integrating discrete and neural features via mixed feature trans dimensional random field language models"):
  with members:
    - "improved training of neural trans dimensional random field language models with dynamic noise contrastive estimation"
    - "language modeling with neural trans dimensional random fields"
    - "model interpolation with trans dimensional random field language models for speech recognition"
    - "efficient softmax approximation for gpus"
    - "improving and scaling trans dimensional random field language models"
    - "adam a method for stochastic optimization"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-2 (induced by source paper "a review of learning with deep generative models from perspective of graphical modeling"):
  with members:
    - "cgans with projection discriminator"
    - "improved training of neural trans dimensional random field language models with dynamic noise contrastive estimation"
    - "the gan landscape losses architectures regularization and normalization"
    - "spectral normalization for generative adversarial networks"
    - "inference suboptimality in variational autoencoders"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "advances in variational inference"
    - "improved variational autoencoders for text modeling using dilated convolutions"

Connected through hyperedge HE-3 (induced by source paper "improved training of neural trans dimensional random field language models with dynamic noise contrastive estimation"):
  with members:
    - "a batch noise contrastive estimation approach for training large vocabulary language models"
    - "language modeling with neural trans dimensional random fields"
    - "efficient softmax approximation for gpus"
    - "on training bi directional neural network language model with noise contrastive estimation"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "one billion word benchmark for measuring progress in statistical language modeling"

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)