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: "fully automated fact checking using external sources"
  Abstract: Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.

Connected through hyperedge HE-1 (induced by source paper "checkthat at clef 2020 enabling the automatic identification and verification of claims in social media"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "it takes nine to smell a rat neural multi task learning for check worthiness prediction"
    - "overview of the clef 2018 checkthat lab on automatic identification and verification of political claims task 1 check worthiness"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "fake news detection on social media a data mining perspective"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "leveraging joint interactions for credibility analysis in news communities"
    - "tweetcred real time credibility assessment of content on twitter"

Connected through hyperedge HE-2 (induced by source paper "detecting toxicity in news articles application to bulgarian"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"
    - "fact checking meets fauxtography verifying claims about images"
    - "fine grained analysis of propaganda in news articles"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "all in one multi task learning for rumour verification"

Connected through hyperedge HE-3 (induced by source paper "taking a stance on fake news towards automatic disinformation assessment via deep bidirectional transformer language models for stance detection"):
  with members:
    - "assessing social and intersectional biases in contextualized word representations"
    - "fakta an automatic end to end fact checking system"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "measuring bias in contextualized word representations"
    - "huggingface s transformers state of the art natural language processing"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "a retrospective analysis of the fake news challenge stance detection task"

Connected through hyperedge HE-4 (induced by source paper "predicting the role of political trolls in social media"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "semeval 2019 task 8 fact checking in community question answering forums"
    - "claimrank detecting check worthy claims in arabic and english"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fact checking in community forums"
    - "an army of me sockpuppets in online discussion communities"

Connected through hyperedge HE-5 (induced by source paper "automatic fact checking using context and discourse information"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "claimrank detecting check worthy claims in arabic and english"
    - "automatic stance detection using end to end memory networks"

Connected through hyperedge HE-6 (induced by source paper "semeval 2019 task 8 fact checking in community question answering forums"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "tmlab srpol at semeval 2019 task 8 fact checking in community question answering forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "semeval 2016 task 3 community question answering"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "predicting factuality of reporting and bias of news media sources"

Connected through hyperedge HE-7 (induced by source paper "adversarial domain adaptation for stance detection"):
  with members:
    - "hunting for troll comments in news community forums"
    - "fever a large scale dataset for fact extraction and verification"
    - "fact checking in community forums"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "a large annotated corpus for learning natural language inference"
    - "deep learning for answer sentence selection"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "fakta an automatic end to end fact checking system"):
  with members:
    - "hunting for troll comments in news community forums"
    - "automatic stance detection using end to end memory networks"
    - "predicting factuality of reporting and bias of news media sources"
    - "fever a large scale dataset for fact extraction and verification"
    - "fact checking in community forums"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "finding streams in knowledge graphs to support fact checking"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"

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)