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: "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"
  Abstract: Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest. Here we introduce a novel approach that makes use of the sequence of transitions observed in tree-structured conversation threads in Twitter. The conversation threads are formed by harvesting users' replies to one another, which results in a nested tree-like structure. Previous work addressing the stance classification task has treated each tweet as a separate unit. Here we analyse tweets by virtue of their position in a sequence and test two sequential classifiers, Linear-Chain CRF and Tree CRF, each of which makes different assumptions about the conversational structure. We experiment with eight Twitter datasets, collected during breaking news, and show that exploiting the sequential structure of Twitter conversations achieves significant improvements over the non-sequential methods. Our work is the first to model Twitter conversations as a tree structure in this manner, introducing a novel way of tackling NLP tasks on Twitter conversations.

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"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "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 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 "how we do things with words analyzing text as social and cultural data"):
  with members:
    - "exploration and exploitation of victorian science in darwin s reading notebooks"
    - "a kernel independence test for geographical language variation"
    - "why should i trust you explaining the predictions of any classifier"
    - "predicting relevance based on assessor disagreement analysis and practical applications for search evaluation"
    - "empath understanding topic signals in large scale text"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "computational sociolinguistics a survey"
    - "gender identity and lexical variation in social media"

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"
    - "simple open stance classification for rumour analysis"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "discourse aware rumour stance classification in social media using sequential classifiers"):
  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"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "using gaussian processes for rumour stance classification in social media"
    - "sub story detection in twitter with hierarchical dirichlet processes"
    - "stance detection with bidirectional conditional encoding"
    - "learning reporting dynamics during breaking news for rumour detection in social media"

Connected through hyperedge HE-7 (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"
    - "simple open stance classification for rumour analysis"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "seminar users in the arabic twitter sphere"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "an army of me sockpuppets in online discussion communities"
    - "characterizing linguistic attributes for automatic classification of intent based racist radicalized posts on tumblr micro blogging website"
    - "online human bot interactions detection estimation and characterization"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "botornot a system to evaluate social bots"
    - "dissecting a social botnet growth content and influence in twitter"

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)