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: "knowledge enhanced contextual word representations"
  Abstract: Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities. We propose a general method to embed multiple knowledge bases (KBs) into large scale models, and thereby enhance their representations with structured, human-curated knowledge. For each KB, we first use an integrated entity linker to retrieve relevant entity embeddings, then update contextual word representations via a form of word-to-entity attention. In contrast to previous approaches, the entity linkers and self-supervised language modeling objective are jointly trained end-to-end in a multitask setting that combines a small amount of entity linking supervision with a large amount of raw text. After integrating WordNet and a subset of Wikipedia into BERT, the knowledge enhanced BERT (KnowBert) demonstrates improved perplexity, ability to recall facts as measured in a probing task and downstream performance on relationship extraction, entity typing, and word sense disambiguation. KnowBert's runtime is comparable to BERT's and it scales to large KBs.

Connected through hyperedge HE-1 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters"):
  with members:
    - "pretrained encyclopedia weakly supervised knowledge pretrained language model"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "kepler a unified model for knowledge embedding and pre trained language representation"
    - "language models as knowledge bases"
    - "parameter efficient transfer learning for nlp"
    - "bert is not a knowledge base yet factual knowledge vs name based reasoning in unsupervised qa"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "matching the blanks distributional similarity for relation learning"

Connected through hyperedge HE-2 (induced by source paper "realm retrieval augmented language model pre training"):
  with members:
    - "how much knowledge can you pack into the parameters of a language model"
    - "generalization through memorization nearest neighbor language models"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "language models as knowledge bases"
    - "latent retrieval for weakly supervised open domain question answering"
    - "knowledge guided text retrieval and reading for open domain question answering"
    - "spanbert improving pre training by representing and predicting spans"
    - "learning to retrieve reasoning paths over wikipedia graph for question answering"

Connected through hyperedge HE-3 (induced by source paper "waldorf wasteless language model distillation on reading comprehension"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "bert rediscovers the classical nlp pipeline"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "do nlp models know numbers probing numeracy in embeddings"
    - "spanbert improving pre training by representing and predicting spans"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-4 (induced by source paper "what does my qa model know devising controlled probes using expert knowledge"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "overcoming catastrophic forgetting by neuron level plasticity control"
    - "abductive commonsense reasoning"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "from f to a on the n y regents science exams an overview of the aristo project"
    - "diversify your datasets analyzing generalization via controlled variance in adversarial datasets"
    - "qasc a dataset for question answering via sentence composition"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"

Connected through hyperedge HE-5 (induced by source paper "pretrained encyclopedia weakly supervised knowledge pretrained language model"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "language models as knowledge bases"
    - "latent retrieval for weakly supervised open domain question answering"
    - "data augmentation for bert fine tuning in open domain question answering"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "knowledge augmented language model and its application to unsupervised named entity recognition"
    - "multi passage bert a globally normalized bert model for open domain question answering"

Connected through hyperedge HE-6 (induced by source paper "kepler a unified model for knowledge embedding and pre trained language representation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "graphvite a high performance cpu gpu hybrid system for node embedding"
    - "language models as knowledge bases"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "multi task deep neural networks for natural language understanding"
    - "matching the blanks distributional similarity for relation learning"

Connected through hyperedge HE-7 (induced by source paper "towards generalizable neuro symbolic systems for commonsense question answering"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "improving question answering with external knowledge"
    - "augmenting neural networks with first order logic"
    - "option comparison network for multiple choice reading comprehension"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "sensebert driving some sense into bert"

Connected through hyperedge HE-8 (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"

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)