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: "wic the word in context dataset for evaluating context sensitive meaning representations"
  Abstract: By design, word embeddings are unable to model the dynamic nature of words' semantics, i.e., the property of words to correspond to potentially different meanings. To address this limitation, dozens of specialized meaning representation techniques such as sense or contextualized embeddings have been proposed. However, despite the popularity of research on this topic, very few evaluation benchmarks exist that specifically focus on the dynamic semantics of words. In this paper we show that existing models have surpassed the performance ceiling of the standard evaluation dataset for the purpose, i.e., Stanford Contextual Word Similarity, and highlight its shortcomings. To address the lack of a suitable benchmark, we put forward a large-scale Word in Context dataset, called WiC, based on annotations curated by experts, for generic evaluation of context-sensitive representations. WiC is released in https://pilehvar.github.io/wic/.

Connected through hyperedge HE-1 (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"
    - "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"
    - "superglue a stickier benchmark for general purpose language understanding systems"

Connected through hyperedge HE-2 (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"
    - "designing and interpreting probes with control tasks"

Connected through hyperedge HE-3 (induced by source paper "huggingface s transformers state of the art natural language processing"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "are sixteen heads really better than one"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "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"
    - "the curious case of neural text degeneration"

Connected through hyperedge HE-4 (induced by source paper "on the cross lingual transferability of monolingual representations"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "parameter efficient transfer learning for nlp"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"
    - "bilingual lexicon induction through unsupervised machine translation"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "paws x a cross lingual adversarial dataset for paraphrase identification"

Connected through hyperedge HE-5 (induced by source paper "slice based learning a programming model for residual learning in critical data slices"):
  with members:
    - "overton a data system for monitoring and improving machine learned products"
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural network acceptability judgments"

Connected through hyperedge HE-6 (induced by source paper "liaad at semdeep 5 challenge word in context wic"):
  with members:
    - "language modelling makes sense propagating representations through wordnet for full coverage word sense disambiguation"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "cosimlex a resource for evaluating graded word similarity in context"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "efficient estimation of word representations in vector space"

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)