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: "bert is not a knowledge base yet factual knowledge vs name based reasoning in unsupervised qa"
  Abstract: The BERT language model (LM) (Devlin et al., 2019) is surprisingly good at answering cloze-style questions about relational facts. Petroni et al. (2019) take this as evidence that BERT memorizes factual knowledge during pre-training. We take issue with this interpretation and argue that the performance of BERT is partly due to reasoning about (the surface form of) entity names, e.g., guessing that a person with an Italian-sounding name speaks Italian. More specifically, we show that BERT's precision drops dramatically when we filter certain easy-to-guess facts. As a remedy, we propose E-BERT, an extension of BERT that replaces entity mentions with symbolic entity embeddings. E-BERT outperforms both BERT and ERNIE (Zhang et al., 2019) on hard-to-guess queries. We take this as evidence that E-BERT is richer in factual knowledge, and we show two ways of ensembling BERT and E-BERT.

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"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "matching the blanks distributional similarity for relation learning"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-2 (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"
    - "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"
    - "knowledge enhanced contextual word representations"

Connected through hyperedge HE-3 (induced by source paper "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"):
  with members:
    - "an open world extension to knowledge graph completion models"
    - "ernie enhanced language representation with informative entities"
    - "knowledge enhanced contextual word representations"
    - "yelm end to end contextualized entity linking"
    - "on the coherence of fake news articles"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"

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)