human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "automatic speech recognition with very large conversational finnish and estonian vocabularies"):
  Abstract: Today, the vocabulary size for language models in large vocabulary speech recognition is typically several hundreds of thousands of words. While this is already sufficient in some applications, the out-of-vocabulary words are still limiting the usability in others. In agglutinative languages the vocabulary for conversational speech should include millions of word forms to cover the spelling variations due to colloquial pronunciations, in addition to the word compounding and inflections. Very large vocabularies are also needed, for example, when the recognition of rare proper names is important. Previously, very large vocabularies have been efficiently modeled in conventional n-gram language models either by splitting words into subword units or by clustering words into classes. While vocabulary size is not as critical anymore in modern speech recognition systems, training time and memory consumption become an issue when state-of-the-art neural network language models are used. In this paper, we investigate techniques that address the vocabulary size issue by reducing the effective vocabulary size and by processing large vocabularies more efficiently. The experimental results in conversational Finnish and Estonian speech recognition indicate that properly defined word classes improve recognition accuracy. Subword n-gram models are not better on evaluation data than word n-gram models constructed from a vocabulary that includes all the words in the training corpus. However, when recurrent neural network (RNN) language models are used, their ability to utilize long contexts gives a larger gain to subword-based modeling. Our best results are from RNN language models that are based on statistical morphs. We show that the suitable size for a subword vocabulary depends on the language. Using time delay neural network acoustic models, we were able to achieve new state of the art in Finnish and Estonian conversational speech recognition, 27.1% word error rate in the Finnish task and 21.9% in the Estonian task.

Includes members:
  Member "theanolm an extensible toolkit for neural network language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on neural network language models")
  Member "efficient softmax approximation for gpus":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "strategies for training large vocabulary neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pre training tasks for embedding based large scale retrieval")
  Member "training very deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "blackout speeding up recurrent neural network language models with very large vocabularies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hide and tell learning to bridge photo streams for visual storytelling")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "a fast and simple algorithm for training neural probabilistic language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition")

Question: Please tell me which class the source paper 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)