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: "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.

Connected through hyperedge HE-1 (induced by source paper "transition based dependency parsing using perceptron learner"):
  with members:
    - "a correspondence analysis framework for author conference recommendations"
    - "modeling product search relevance in e commerce"
    - "simultaneous identification of tweet purpose and position"
    - "a machine learning framework for authorship identification from texts"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"

Connected through hyperedge HE-2 (induced by source paper "modeling product search relevance in e commerce"):
  with members:
    - "a correspondence analysis framework for author conference recommendations"
    - "simultaneous identification of tweet purpose and position"
    - "a machine learning framework for authorship identification from texts"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"

Connected through hyperedge HE-3 (induced by source paper "a correspondence analysis framework for author conference recommendations"):
  with members:
    - "simultaneous identification of tweet purpose and position"
    - "a machine learning framework for authorship identification from texts"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"

Connected through hyperedge HE-4 (induced by source paper "a heterogeneous graphical model to understand user level sentiments in social media"):
  with members:
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "content based video indexing and retrieval using corr lda"
    - "user level sentiment analysis incorporating social networks"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-5 (induced by source paper "a machine learning framework for authorship identification from texts"):
  with members:
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "content based video indexing and retrieval using corr lda"

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)