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 "10sent a stable sentiment analysis method based on the combination of off the shelf approaches"):
  Abstract: Sentiment analysis has become a very important tool for analysis of social media data. There are several methods developed for this research field, many of them working very differently from each other, covering distinct aspects of the problem and disparate strategies. Despite the large number of existent techniques, there is no single one which fits well in all cases or for all data sources. Supervised approaches may be able to adapt to specific situations but they require manually labeled training, which is very cumbersome and expensive to acquire, mainly for a new application. In this context, in here, we propose to combine several very popular and effective state-of-the-practice sentiment analysis methods, by means of an unsupervised bootstrapped strategy for polarity classification. One of our main goals is to reduce the large variability (lack of stability) of the unsupervised methods across different domains (datasets). Our solution was thoroughly tested considering thirteen different datasets in several domains such as opinions, comments, and social media. The experimental results demonstrate that our combined method (aka, 10SENT) improves the effectiveness of the classification task, but more importantly, it solves a key problem in the field. It is consistently among the best methods in many data types, meaning that it can produce the best (or close to best) results in almost all considered contexts, without any additional costs (e.g., manual labeling). Our self-learning approach is also very independent of the base methods, which means that it is highly extensible to incorporate any new additional method that can be envisioned in the future. Finally, we also investigate a transfer learning approach for sentiment analysis as a means to gather additional (unsupervised) information for the proposed approach and we show the potential of this technique to improve our results.

Includes members:
  Member "deep learning for sentiment analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an end to end visual audio attention network for emotion recognition in user generated videos")
  Member "leveraging large amounts of weakly supervised data for multi language sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition")
  Member "comparing and combining sentiment analysis methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling spatiotemporal factors associated with sentiment on twitter synthesis and suggestions for improving the identification of localized deviations")
  Member "crowdsourcing a word emotion association lexicon":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "text classification with lexicon from preattention mechanism")
  Member "wikisent weakly supervised sentiment analysis through extractive summarization with wikipedia":
    also connected through other hyperedges:
      (none)
  Member "a new anew evaluation of a word list for sentiment analysis in microblogs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how impersonators exploit instagram to generate fake engagement")
  Member "twitter mood predicts the stock market":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mining social media data for biomedical signals and health related behavior")
  Member "thumbs up sentiment classification using machine learning techniques":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "description based text classification with reinforcement learning")

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)