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: "alternative structures for character level rnns"
  Abstract: Recurrent neural networks are convenient and efficient models for language modeling. However, when applied on the level of characters instead of words, they suffer from several problems. In order to successfully model long-term dependencies, the hidden representation needs to be large. This in turn implies higher computational costs, which can become prohibitive in practice. We propose two alternative structural modifications to the classical RNN model. The first one consists on conditioning the character level representation on the previous word representation. The other one uses the character history to condition the output probability. We evaluate the performance of the two proposed modifications on challenging, multi-lingual real world data.

Connected through hyperedge HE-1 (induced by source paper "tabula nearly rasa probing the linguistic knowledge of character level neural language models trained on unsegmented text"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "explaining character aware neural networks for word level prediction do they discover linguistic rules"
    - "assessing composition in sentence vector representations"
    - "an analysis of neural language modeling at multiple scales"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-2 (induced by source paper "parsimonious morpheme segmentation with an application to enriching word embeddings"):
  with members:
    - "morphological word embeddings"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "bag of tricks for efficient text classification"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "bidirectional lstm crf models for sequence tagging"
    - "character aware neural language models"

Connected through hyperedge HE-3 (induced by source paper "a subword level language model for bangla language"):
  with members:
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "regularizing and optimizing lstm language models"
    - "sgdr stochastic gradient descent with restarts"
    - "automated word prediction in bangla language using stochastic language models"
    - "no more pesky learning rate guessing games"

Connected through hyperedge HE-4 (induced by source paper "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"):
  with members:
    - "beyond heuristics learning visualization design"
    - "vizml a machine learning approach to visualization recommendation"
    - "tree to tree neural networks for program translation"
    - "foresight recommending visual insights"
    - "progressive growing of gans for improved quality stability and variation"
    - "pix2code generating code from a graphical user interface screenshot"
    - "massive exploration of neural machine translation architectures"
    - "task based effectiveness of basic visualizations"

Connected through hyperedge HE-5 (induced by source paper "character word lstm language models"):
  with members:
    - "character based neural machine translation"
    - "neural language correction with character based attention"
    - "gated word character recurrent language model"
    - "exploring the limits of language modeling"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character based neural machine translation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "character aware neural language models"

Connected through hyperedge HE-6 (induced by source paper "enriching word vectors with subword information"):
  with members:
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "new word analogy corpus for exploring embeddings of czech words"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "character aware neural language models"
    - "neural machine translation of rare words with subword units"
    - "compositional morphology for word representations and language modelling"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "gated word character recurrent language model"):
  with members:
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "adaptive joint learning of compositional and non compositional phrase embeddings"
    - "neural architectures for named entity recognition"
    - "exploring the limits of language modeling"
    - "theano a python framework for fast computation of mathematical expressions"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "character aware neural language models"
    - "recurrent neural network regularization"

Connected through hyperedge HE-8 (induced by source paper "neural machine transliteration preliminary results"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character based neural machine translation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "a comparison of different machine transliteration models"

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)