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: "reference aware language models"
  Abstract: We propose a general class of language models that treat reference as an explicit stochastic latent variable. This architecture allows models to create mentions of entities and their attributes by accessing external databases (required by, e.g., dialogue generation and recipe generation) and internal state (required by, e.g. language models which are aware of coreference). This facilitates the incorporation of information that can be accessed in predictable locations in databases or discourse context, even when the targets of the reference may be rare words. Experiments on three tasks shows our model variants based on deterministic attention.

Connected through hyperedge HE-1 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-2 (induced by source paper "knowledge enhanced contextual word representations"):
  with members:
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "tucker tensor factorization for knowledge graph completion"
    - "simple bert models for relation extraction and semantic role labeling"

Connected through hyperedge HE-3 (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-4 (induced by source paper "toward unsupervised text content manipulation"):
  with members:
    - "text infilling"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "unsupervised text style transfer using language models as discriminators"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "structured content preservation for unsupervised text style transfer"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-5 (induced by source paper "how can we know what language models know"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "bert is not a knowledge base yet factual knowledge vs name based reasoning in unsupervised qa"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "mask predict parallel decoding of conditional masked language models"
    - "matching the blanks distributional similarity for relation learning"
    - "latent relation language models"

Connected through hyperedge HE-6 (induced by source paper "generating personalized recipes from historical user preferences"):
  with members:
    - "sequence aware recommender systems"
    - "discourse aware neural rewards for coherent text generation"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "generating more interesting responses in neural conversation models with distributional constraints"

Connected through hyperedge HE-7 (induced by source paper "pomo generating entity specific post modifiers in context"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "attention is all you need"
    - "a neural knowledge language model"
    - "challenges in data to document generation"

Connected through hyperedge HE-8 (induced by source paper "what do entity centric models learn insights from entity linking in multi party dialogue"):
  with members:
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "amore upf at semeval 2018 task 4 bilstm with entity library"
    - "evaluating theory of mind in question answering"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "relnet end to end modeling of entities relations"
    - "improving coreference resolution by learning entity level distributed representations"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "representation of linguistic form and function in 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)