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: "simultaneous identification of tweet purpose and position"
  Abstract: Tweet classification has attracted considerable attention recently. Most of the existing work on tweet classification focuses on topic classification, which classifies tweets into several predefined categories, and sentiment classification, which classifies tweets into positive, negative and neutral. Since tweets are different from conventional text in that they generally are of limited length and contain informal, irregular or new words, so it is difficult to determine user intention to publish a tweet and user attitude towards certain topic. In this paper, we aim to simultaneously classify tweet purpose, i.e., the intention for user to publish a tweet, and position, i.e., supporting, opposing or being neutral to a given topic. By transforming this problem to a multi-label classification problem, a multi-label classification method with post-processing is proposed. Experiments on real-world data sets demonstrate the effectiveness of this method and the results outperform the individual classification methods.

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"
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "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"
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "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:
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "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"

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)