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: "language modeling with neural trans dimensional random fields"
  Abstract: Trans-dimensional random field language models (TRF LMs) have recently been introduced, where sentences are modeled as a collection of random fields. The TRF approach has been shown to have the advantages of being computationally more efficient in inference than LSTM LMs with close performance and being able to flexibly integrating rich features. In this paper we propose neural TRFs, beyond of the previous discrete TRFs that only use linear potentials with discrete features. The idea is to use nonlinear potentials with continuous features, implemented by neural networks (NNs), in the TRF framework. Neural TRFs combine the advantages of both NNs and TRFs. The benefits of word embedding, nonlinear feature learning and larger context modeling are inherited from the use of NNs. At the same time, the strength of efficient inference by avoiding expensive softmax is preserved. A number of technical contributions, including employing deep convolutional neural networks (CNNs) to define the potentials and incorporating the joint stochastic approximation (JSA) strategy in the training algorithm, are developed in this work, which enable us to successfully train neural TRF LMs. Various LMs are evaluated in terms of speech recognition WERs by rescoring the 1000-best lists of WSJ'92 test data. The results show that neural TRF LMs not only improve over discrete TRF LMs, but also perform slightly better than LSTM LMs with only one fifth of parameters and 16x faster inference efficiency.

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"
    - "learning neural trans dimensional random field language models with noise contrastive estimation"
    - "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"
    - "learning neural trans dimensional random field language models with noise contrastive estimation"
    - "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)