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: "discourse aware rumour stance classification in social media using sequential classifiers"
  Abstract: Abstract   Rumour stance classification, defined as classifying the stance of specific social media posts into one of supporting, denying, querying or commenting on an earlier post, is becoming of increasing interest to researchers. While most previous work has focused on using individual tweets as classifier inputs, here we report on the performance of sequential classifiers that exploit the discourse features inherent in social media interactions or ‘conversational threads’. Testing the effectiveness of four sequential classifiers – Hawkes Processes, Linear-Chain Conditional Random Fields (Linear CRF), Tree-Structured Conditional Random Fields (Tree CRF) and Long Short Term Memory networks (LSTM) – on eight datasets associated with breaking news stories, and looking at different types of local and contextual features, our work sheds new light on the development of accurate stance classifiers. We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers. Furthermore, we show that LSTM using a reduced set of features can outperform the other sequential classifiers; this performance is consistent across datasets and across types of stances. To conclude, our work also analyses the different features under study, identifying those that best help characterise and distinguish between stances, such as supporting tweets being more likely to be accompanied by evidence than denying tweets. We also set forth a number of directions for future research.

Connected through hyperedge HE-1 (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"
    - "twowingos a two wing optimization strategy for evidential claim verification"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

Connected through hyperedge HE-2 (induced by source paper "your stance is exposed analysing possible factors for stance detection on social media"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "assessing sentiment of the expressed stance on social media"
    - "automatic stance detection using end to end memory networks"
    - "topical stance detection for twitter a two phase lstm model using attention"
    - "hiding individuals and communities in a social network"
    - "friends and enemies of clinton and trump using context for detecting stance in political tweets"
    - "reducing controversy by connecting opposing views"
    - "stance detection with bidirectional conditional encoding"

Connected through hyperedge HE-3 (induced by source paper "mapping dis information flow about the mh17 plane crash"):
  with members:
    - "universal dependency parsing from scratch"
    - "issue framing in online discussion fora"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "junk news on military affairs and national security social media disinformation campaigns against us military personnel and veterans"
    - "framing and agenda setting in russian news a computational analysis of intricate political strategies"
    - "neural discourse structure for text categorization"
    - "tweet2vec character based distributed representations for social media"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-4 (induced by source paper "scilens evaluating the quality of scientific news articles using social media and scientific literature indicators"):
  with members:
    - "predicting factuality of reporting and bias of news media sources"
    - "quootstrap scalable unsupervised extraction of quotation speaker pairs from large news corpora via bootstrapping"
    - "fake news detection on social media a data mining perspective"
    - "learning to identify ambiguous and misleading news headlines"
    - "hoaxy a platform for tracking online misinformation"
    - "stance and sentiment in tweets"
    - "computational fact checking from knowledge networks"
    - "characterizing the life cycle of online news stories using social media reactions"

Connected through hyperedge HE-5 (induced by source paper "rumor detection on social media datasets methods and opportunities"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "automatic rumor detection on microblogs a survey"
    - "beyond news contents the role of social context for fake news detection"
    - "fake news detection on social media a data mining perspective"
    - "some like it hoax automated fake news detection in social networks"
    - "spotting rumors via novelty detection"

Connected through hyperedge HE-6 (induced by source paper "mining social media for newsgathering a review"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "supporting the use of user generated content in journalistic practice"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "fake news detection on social media a data mining perspective"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "hoaxy a platform for tracking online misinformation"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "computational fact checking from knowledge networks"

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)