human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "stance classification for rumour analysis in twitter exploiting affective information and conversation structure"):
  Abstract: Analysing how people react to rumours associated with news in social media is an important task to prevent the spreading of misinformation, which is nowadays widely recognized as a dangerous tendency. In social media conversations, users show different stances and attitudes towards rumourous stories. Some users take a definite stance, supporting or denying the rumour at issue, while others just comment it, or ask for additional evidence related to the veracity of the rumour. On this line, a new shared task has been proposed at SemEval-2017 (Task 8, SubTask A), which is focused on rumour stance classification in English tweets. The goal is predicting user stance towards emerging rumours in Twitter, in terms of supporting, denying, querying, or commenting the original rumour, looking at the conversation threads originated by the rumour. This paper describes a new approach to this task, where the use of conversation-based and affective-based features, covering different facets of affect, has been explored. Our classification model outperforms the best-performing systems for stance classification at SemEval-2017 Task 8, showing the effectiveness of the feature set proposed.

Includes members:
  Member "friends and enemies of clinton and trump using context for detecting stance in political tweets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "your stance is exposed analysing possible factors for stance detection on social media")
  Member "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "simple open stance classification for rumour analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling conversation structure and temporal dynamics for jointly predicting rumor stance and veracity")
  Member "fake news detection on social media a data mining perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a semi supervised approach to message stance classification")
  Member "analysing how people orient to and spread rumours in social media by looking at conversational threads":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "two path deep semi supervised learning for timely fake news detection")
  Member "towards detecting rumours in social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities")
  Member "crowdsourcing a word emotion association lexicon":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "text classification with lexicon from preattention mechanism")

Question: Please tell me which class the source paper 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)