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: "named entity disambiguation for noisy text"
  Abstract: We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and more challenging than existing news-based datasets. To capture the limited and noisy local context surrounding each mention, we design a neural model and train it with a novel method for sampling informative negative examples. We also describe a new way of initializing word and entity embeddings that significantly improves performance. Our model significantly outperforms existing state-of-the-art methods on WikilinksNED while achieving comparable performance on a smaller newswire dataset.

Connected through hyperedge HE-1 (induced by source paper "wikipedia2vec an efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia"):
  with members:
    - "ernie enhanced language representation with informative entities"
    - "yelm end to end contextualized entity linking"
    - "neural attentive bag of entities model for text classification"
    - "studio ousia s quiz bowl question answering system"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "learning distributed representations of texts and entities from knowledge base"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-2 (induced by source paper "neural attentive bag of entities model for text classification"):
  with members:
    - "neural collective entity linking"
    - "end to end neural entity linking"
    - "studio ousia s quiz bowl question answering system"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "learning distributed representations of texts and entities from knowledge base"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-3 (induced by source paper "yelm end to end contextualized entity linking"):
  with members:
    - "joint learning of named entity recognition and entity linking"
    - "pre training of deep contextualized embeddings of words and entities for named entity disambiguation"
    - "end to end neural entity linking"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"
    - "attention is all you need"
    - "learning distributed representations of texts and entities from knowledge base"

Connected through hyperedge HE-4 (induced by source paper "fine grained entity typing for domain independent entity linking"):
  with members:
    - "learning to denoise distantly labeled data for entity typing"
    - "boosting entity linking performance by leveraging unlabeled documents"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-5 (induced by source paper "learning dense representations for entity retrieval"):
  with members:
    - "end to end retrieval in continuous space"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "efficient natural language response suggestion for smart reply"
    - "learning distributed representations of texts and entities from knowledge base"
    - "deep joint entity disambiguation with local neural attention"
    - "neural cross lingual entity linking"
    - "dynamic entity representations in neural language models"
    - "capturing semantic similarity for entity linking with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "pre training of deep contextualized embeddings of words and entities for named entity disambiguation"):
  with members:
    - "neural collective entity linking"
    - "improving entity linking by modeling latent relations between mentions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "collective entity disambiguation with structured gradient tree boosting"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "learning distributed representations of texts and entities from knowledge base"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-7 (induced by source paper "from word to sense embeddings a survey on vector representations of meaning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "seven augmenting word embeddings with unsupervised relation vectors"
    - "incorporating glosses into neural word sense disambiguation"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "knowledge based word sense disambiguation using topic models"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-8 (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"
    - "bert is not a knowledge base yet factual knowledge vs name based reasoning in unsupervised qa"
    - "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"

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)