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: "simple open stance classification for rumour analysis"
  Abstract: Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.

Connected through hyperedge HE-1 (induced by source paper "modeling conversation structure and temporal dynamics for jointly predicting rumor stance and veracity"):
  with members:
    - "a semi supervised approach to message stance classification"
    - "stance classification for rumour analysis in twitter exploiting affective information and conversation structure"
    - "combating fake news a survey on identification and mitigation techniques"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "all in one multi task learning for rumour verification"
    - "identifying and understanding user reactions to deceptive and trusted social news sources"
    - "cross lingual cross platform rumor verification pivoting on multimedia content"
    - "declare debunking fake news and false claims using evidence aware deep learning"

Connected through hyperedge HE-2 (induced by source paper "danish stance classification and rumour resolution"):
  with members:
    - "fake news detection using stance classification a survey"
    - "but fit at semeval 2019 task 7 determining the rumour stance with pre trained deep bidirectional transformers"
    - "clearumor at semeval 2019 task 7 convolving elmo against rumors"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the fact extraction and verification fever shared task"
    - "detection and resolution of rumours in social media a survey"
    - "learning word vectors for 157 languages"
    - "rumoureval 2019 determining rumour veracity and support for rumours"

Connected through hyperedge HE-3 (induced by source paper "fake news detection using stance classification a survey"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "beyond news contents the role of social context for fake news detection"
    - "fake news detection on social media a data mining perspective"
    - "stance detection with bidirectional conditional encoding"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "microblog analysis as a programme of work"

Connected through hyperedge HE-4 (induced by source paper "stance classification for rumour analysis in twitter exploiting affective information and conversation structure"):
  with members:
    - "friends and enemies of clinton and trump using context for detecting stance in political tweets"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "fake news detection on social media a data mining perspective"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "towards detecting rumours in social media"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-5 (induced by source paper "detection and resolution of rumours in social media a survey"):
  with members:
    - "rumor detection and classification for twitter data"
    - "supporting the use of user generated content in journalistic practice"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "determining the veracity of rumours on twitter"
    - "hoaxy a platform for tracking online misinformation"
    - "contradiction detection for rumorous claims"

Connected through hyperedge HE-6 (induced by source paper "stance prediction for russian data and analysis"):
  with members:
    - "fever a large scale dataset for fact extraction and verification"
    - "detection and resolution of rumours in social media a survey"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "stance detection in online discussions"
    - "bilateral multi perspective matching for natural language sentences"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "hoaxy a platform for tracking online misinformation"

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)