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: "analysis of named entity recognition and linking for tweets"
  Abstract: Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.

Connected through hyperedge HE-1 (induced by source paper "benchmarking knowledge graphs on the web"):
  with members:
    - "finding streams in knowledge graphs to support fact checking"
    - "bengal an automatic benchmark generator for entity recognition and linking"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "thousands of debitcredit transactions per second easy and inexpensive"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "competing topic naming conventions in quora predicting appropriate topic merges and winning topics from millions of topic pairs"):
  with members:
    - "universal sentence encoder"
    - "deep dive into anonymity a large scale analysis of quora questions"
    - "language use matters analysis of the linguistic structure of question texts can characterize answerability in quora"
    - "competition and selection among conventions"
    - "bieber blast bieberblast early prediction of popular hashtag compounds"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "who is authoritative understanding reputation mechanisms in quora"

Connected through hyperedge HE-3 (induced by source paper "named entity recognition on code switched data overview of the calcs 2018 shared task"):
  with members:
    - "part of speech tagging for code switched data"
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "a multi task approach for named entity recognition in social media data"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "recognizing musical entities in user generated content"):
  with members:
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "neural architectures for named entity recognition"
    - "fast and accurate annotation of short texts with wikipedia pages"

Connected through hyperedge HE-5 (induced by source paper "simple natural language processing tools for danish"):
  with members:
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-6 (induced by source paper "relic entity profiling using random forest and trustworthiness of a source"):
  with members:
    - "distributed representations of words and phrases and their compositionality"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-7 (induced by source paper "a survey of location prediction on twitter"):
  with members:
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "a neural model for user geolocation and lexical dialectology"
    - "density estimation for geolocation via convolutional mixture density network"
    - "a survey of point of interest recommendation in location based social networks"
    - "exploiting text and network context for geolocation of social media users"
    - "twitter user geolocation using a unified text and network prediction model"
    - "a survey of location inference techniques on twitter"
    - "geo located twitter as proxy for global mobility patterns"

Connected through hyperedge HE-8 (induced by source paper "end to end neural entity linking"):
  with members:
    - "improving entity linking by modeling latent relations between mentions"
    - "collective entity disambiguation with structured gradient tree boosting"
    - "end to end neural coreference resolution"
    - "deep joint entity disambiguation with local neural attention"
    - "dynamic entity representations in neural language models"
    - "empower sequence labeling with task aware neural language model"
    - "neural architectures for named entity recognition"
    - "joint learning of the embedding of words and entities for named entity disambiguation"

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)