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: "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
  Abstract: This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A). Subtask A addresses the challenge of rumour stance classification, which involves identifying the attitude of Twitter users towards the truthfulness of the rumour they are discussing. Stance classification is considered to be an important step towards rumour verification, therefore performing well in this task is expected to be useful in debunking false rumours. In this work we classify a set of Twitter posts discussing rumours into either supporting, denying, questioning or commenting on the underlying rumours. We propose a LSTM-based sequential model that, through modelling the conversational structure of tweets, which achieves an accuracy of 0.784 on the RumourEval test set outperforming all other systems in Subtask A.

Connected through hyperedge HE-1 (induced by source paper "a semi supervised approach to message stance classification"):
  with members:
    - "rumor detection and classification for twitter data"
    - "detection and resolution of rumours in social media a survey"
    - "fake news detection on social media a data mining perspective"
    - "determining the veracity of rumours on twitter"
    - "using gaussian processes for rumour stance classification in social media"
    - "a tutorial on online supervised learning with applications to node classification in social networks"
    - "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-2 (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-3 (induced by source paper "different absorption from the same sharing sifted multi task learning for fake news detection"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "all in one multi task learning for rumour verification"
    - "automatic stance detection using end to end memory networks"
    - "zero resource multilingual model transfer learning what to share"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "a survey on natural language processing for fake news detection"
    - "multi source cross lingual model transfer learning what to share"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "multifc a real world multi domain dataset for evidence based fact checking of claims"):
  with members:
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "end to end neural entity linking"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "twowingos a two wing optimization strategy for evidential claim verification"

Connected through hyperedge HE-5 (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-6 (induced by source paper "back to the future sequential alignment of text representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "a review of domain adaptation without target labels"
    - "deep contextualized word representations"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rumoureval 2019 determining rumour veracity and support for rumours"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"

Connected through hyperedge HE-7 (induced by source paper "but fit at semeval 2019 task 7 determining the rumour stance with pre trained deep bidirectional transformers"):
  with members:
    - "automatic stance detection using end to end memory networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a structured self attentive sentence embedding"
    - "stance detection with bidirectional conditional encoding"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "nrc canada building the state of the art in sentiment analysis of tweets"

Connected through hyperedge HE-8 (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"
    - "simple open stance classification for rumour analysis"
    - "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"
    - "towards detecting rumours in social media"
    - "crowdsourcing a word emotion association lexicon"

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)