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 "an unsupervised domain independent framework for automated detection of persuasion tactics in text"):
  Abstract: With the increasing growth of social media, people have started relying heavily on the information shared therein to form opinions and make decisions. While such a reliance is motivation for a variety of parties to promote information, it also makes people vulnerable to exploitation by slander, misinformation, terroristic and predatorial advances. In this work, we aim to understand and detect such attempts at persuasion. Existing works on detecting persuasion in text make use of lexical features for detecting persuasive tactics, without taking advantage of the possible structures inherent in the tactics used. We formulate the task as a multi-class classification problem and propose an unsupervised, domain-independent machine learning framework for detecting the type of persuasion used in text, which exploits the inherent sentence structure present in the different persuasion tactics. Our work shows promising results as compared to existing work.

Includes members:
  Member "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transition based dependency parsing using perceptron learner")
  Member "object sensitive deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised object level deep reinforcement learning")
  Member "transparency and explanation in deep reinforcement learning neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploratory not explanatory counterfactual analysis of saliency maps for deep reinforcement learning")
  Member "photorealistic style transfer for videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transition based dependency parsing using perceptron learner")
  Member "argument strength is in the eye of the beholder audience effects in persuasion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process")
  Member "joint embedding of hierarchical categories and entities for concept categorization and dataless classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "wikipedia2vec an efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia")
  Member "winning arguments interaction dynamics and persuasion strategies in good faith online discussions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "content based video indexing and retrieval using corr lda":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transition based dependency parsing using perceptron learner")

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)