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: "generalizing and hybridizing count based and neural language models"
  Abstract: Language models (LMs) are statistical models that calculate probabilities over sequences of words or other discrete symbols. Currently two major paradigms for language modeling exist: count-based n-gram models, which have advantages of scalability and test-time speed, and neural LMs, which often achieve superior modeling performance. We demonstrate how both varieties of models can be unified in a single modeling framework that defines a set of probability distributions over the vocabulary of words, and then dynamically calculates mixture weights over these distributions. This formulation allows us to create novel hybrid models that combine the desirable features of count-based and neural LMs, and experiments demonstrate the advantages of these approaches.

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"
    - "neural lattice language models"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "stem driven language models for morphologically rich languages"):
  with members:
    - "morphological segmentation inside out"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "from characters to words to in between do we capture morphology"
    - "character word lstm language models"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "a joint model for word embedding and word morphology"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "lightweight adaptive mixture of neural and n gram language models"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "efficient softmax approximation for gpus"
    - "language modeling with gated convolutional networks"
    - "exploring the limits of language modeling"
    - "adam a method for stochastic optimization"
    - "on using very large target vocabulary for neural machine translation"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-4 (induced by source paper "neural machine translation and sequence to sequence models a tutorial"):
  with members:
    - "lstm a search space odyssey"
    - "online segment to segment neural transduction"
    - "sequence to sequence learning as beam search optimization"
    - "memory enhanced decoder for neural machine translation"
    - "learning to translate in real time with neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "recurrent neural network grammars"
    - "coverage embedding models for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "breaking the softmax bottleneck a high rank rnn language model"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "dynamic evaluation of neural sequence models"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "recurrent highway networks"
    - "improving neural language models with a continuous cache"

Connected through hyperedge HE-6 (induced by source paper "improving context aware language models"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "a persona based neural conversation model"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "towards a continuous modeling of natural language domains"
    - "characterizing the language of online communities and its relation to community reception"
    - "using the output embedding to improve language models"
    - "context aware natural language generation with recurrent 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)