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: "matching the blanks distributional similarity for relation learning"
  Abstract: General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their surface forms, or which jointly embed surface forms with relations from an existing knowledge graph. However, both of these approaches are limited in their ability to generalize. In this paper, we build on extensions of Harris' distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text. We show that these representations significantly outperform previous work on exemplar based relation extraction (FewRel) even without using any of that task's training data. We also show that models initialized with our task agnostic representations, and then tuned on supervised relation extraction datasets, significantly outperform the previous methods on SemEval 2010 Task 8, KBP37, and TACRED.

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"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-2 (induced by source paper "meta learning with dynamic memory based prototypical network for few shot event detection"):
  with members:
    - "exploiting contextual information via dynamic memory network for event detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diverse few shot text classification with multiple metrics"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "matching networks for one shot learning"
    - "dynamic memory networks for visual and textual question answering"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-3 (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"
    - "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"
    - "superglue a stickier benchmark for general purpose language understanding systems"

Connected through hyperedge HE-4 (induced by source paper "spanbert improving pre training by representing and predicting spans"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "ernie enhanced representation through knowledge integration"
    - "mass masked sequence to sequence pre training for language generation"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ernie enhanced language representation with informative entities"
    - "kermit generative insertion based modeling for sequences"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-5 (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"
    - "latent relation language models"

Connected through hyperedge HE-6 (induced by source paper "blockwise self attention for long document understanding"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "latent retrieval for weakly supervised open domain question answering"
    - "adaptively sparse transformers"
    - "passage re ranking with bert"
    - "spanbert improving pre training by representing and predicting spans"
    - "low memory neural network training a technical report"
    - "transformer xl attentive language models beyond a fixed length context"
    - "bert for coreference resolution baselines and analysis"

Connected through hyperedge HE-7 (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"
    - "latent relation language models"
    - "knowledge enhanced contextual word representations"

Connected through hyperedge HE-8 (induced by source paper "sentilr linguistic knowledge enhanced language representation for sentiment analysis"):
  with members:
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "grammatical analysis of pretrained sentence encoders with acceptability judgments"
    - "mass masked sequence to sequence pre training for language generation"
    - "ernie enhanced language representation with informative entities"
    - "story ending prediction by transferable bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

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)