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: "neural lattice language models"
  Abstract: In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language models. These models construct a lattice of possible paths through a sentence and marginalize across this lattice to calculate sequence probabilities or optimize parameters. This approach allows us to seamlessly incorporate linguistic intuitions - including polysemy and existence of multi-word lexical items - into our language model. Experiments on multiple language modeling tasks show that English neural lattice language models that utilize polysemous embeddings are able to improve perplexity by 9.95% relative to a word-level baseline, and that a Chinese model that handles multi-character tokens is able to improve perplexity by 20.94% relative to a character-level baseline.

Connected through hyperedge HE-1 (induced by source paper "latent relation language models"):
  with members:
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "bootstrapping generators from noisy data"
    - "describing a knowledge base"
    - "deep contextualized word representations"
    - "building language models for text with named entities"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "learning to compute word embeddings on the fly"

Connected through hyperedge HE-2 (induced by source paper "confusion2vec towards enriching vector space word representations with representational ambiguities"):
  with members:
    - "morphological word embeddings"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "neural lattice to sequence models for uncertain inputs"
    - "enriching word vectors with subword information"
    - "multi view recurrent neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "bag of tricks for efficient text classification"
    - "lattice based recurrent neural network encoders for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "lattice transformer for speech translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised multi modal neural machine translation"
    - "chinese ner using lattice lstm"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "bilingual expert can find translation errors"
    - "self attention with relative position representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "adaptive input representations for neural language modeling"):
  with members:
    - "scaling neural machine translation"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "character level language modeling with deeper self attention"
    - "attention is all you need"
    - "sgdr stochastic gradient descent with restarts"
    - "efficient softmax approximation for gpus"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "interpretable textual neuron representations for nlp"):
  with members:
    - "representations of language in a model of visually grounded speech signal"
    - "interpreting neural networks to improve politeness comprehension"
    - "representation of linguistic form and function in recurrent neural networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-6 (induced by source paper "tangent automatic differentiation using source code transformation for dynamically typed array programming"):
  with members:
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "theano a python framework for fast computation of mathematical expressions"
    - "automatic differentiation in machine learning a survey"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

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)