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 "the evolution of argumentation mining from models to social media and emerging tools"):
  Abstract: Abstract   Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.

Includes members:
  Member "claudette an automated detector of potentially unfair clauses in online terms of service":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the privacy policy landscape after the gdpr")
  Member "argumentation mining exploiting multiple sources and background knowledge":
    also connected through other hyperedges:
      (none)
  Member "multi task learning for argumentation mining in low resource settings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "cross topic argument mining from heterogeneous sources using attention based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tacam topic and context aware argument mining")
  Member "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gradient based adversarial training on transformer networks for detecting check worthy factual claims")
  Member "argumentation mining in user generated web discourse":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "the argument reasoning comprehension task identification and reconstruction of implicit warrants":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning")
  Member "mitre at semeval 2016 task 6 transfer learning for stance detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dan dual view representation learning for adapting stance classifiers to new domains")

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.IR(Information Retrieval)