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: "a batch noise contrastive estimation approach for training large vocabulary language models"
  Abstract: Training large vocabulary Neural Network Language Models (NNLMs) is a difficult task due to the explicit requirement of the output layer normalization, which typically involves the evaluation of the full softmax function over the complete vocabulary. This paper proposes a Batch Noise Contrastive Estimation (B-NCE) approach to alleviate this problem. This is achieved by reducing the vocabulary, at each time step, to the target words in the batch and then replacing the softmax by the noise contrastive estimation approach, where these words play the role of targets and noise samples at the same time. In doing so, the proposed approach can be fully formulated and implemented using optimal dense matrix operations. Applying B-NCE to train different NNLMs on the Large Text Compression Benchmark (LTCB) and the One Billion Word Benchmark (OBWB) shows a significant reduction of the training time with no noticeable degradation of the models performance. This paper also presents a new baseline comparative study of different standard NNLMs on the large OBWB on a single Titan-X GPU.

Connected through hyperedge HE-1 (induced by source paper "improved training of neural trans dimensional random field language models with dynamic noise contrastive estimation"):
  with members:
    - "learning neural trans dimensional random field language models with noise contrastive estimation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "self normalization properties of language modeling"):
  with members:
    - "a simple language model based on pmi matrix approximations"
    - "exploring the limits of language modeling"
    - "pointer sentinel mixture models"
    - "strategies for training large vocabulary neural language models"
    - "blackout speeding up recurrent neural network language models with very large vocabularies"
    - "recurrent neural network regularization"
    - "a fast and simple algorithm for training neural probabilistic language models"

Connected through hyperedge HE-3 (induced by source paper "learning neural trans dimensional random field language models with noise contrastive estimation"):
  with members:
    - "tacotron towards end to end speech synthesis"
    - "joint stochastic approximation learning of helmholtz machines"
    - "exploring the limits of language modeling"
    - "character aware neural language models"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "generative modeling of convolutional neural networks"

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)