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 "crowds on wall street extracting value from social investing platforms"):
  Abstract: For decades, the world of financial advisors has been dom- inated by large investment banks such as Goldman Sachs. In recent years, user-contributed investment services suc h as SeekingAlpha and StockTwits have grown to millions of users. In this paper, we seek to understand the quality and impact of content on social investment platforms, by empiri- cally analyzing complete datasets of SeekingAlpha articles (9 years) and StockTwits messages (4 years). We develop sen- timent analysis tools and correlate contributed content to the historical performance of relevant stocks. While SeekingAl- pha articles and StockTwits messages provide minimal corre- lation to stock performance in aggregate, a subset of authors contribute more valuable (predictive) content. We show that these authors can be identified via both empirical methods or by user interactions, and investments using their analysis sig- nificantly outperform broader markets. Finally, we conduct a user survey that sheds light on users views of SeekingAlpha content and stock manipulation. In this paper, we seek to understand the quality and impact of opinions and analysis shared on social investment platforms. We target the two primary yet quite different social invest- ment platforms, SeekingAlpha and StockTwits, and analyze the potential for investment returns following their recom - mendations versus the market baseline, the S&P 500 stock market index. We seek to understand how expertise of con- tributors can affect the quality and utility of contributed con- tent, using SeekingAlpha as an "expert" model (all content is contributed by less than 0.27% of users) and StockTwits as a "peer" model (any user can contribute). Our work makes four key contributions. First, we gather longitudinal datasets from both platforms since their inception (9 years of data for SeekingAlpha, 4 years for StockTwits). We develop sentiment analyzers on each dataset, using a mixture of keyword processing and machine learning classifiers. Validation shows our methods achieve high accuracy in extracting sentiments towards in- dividual stocks (85.5% for SeekingAlpha, 76.2% for Stock- Twits). Second, we analyze correlation between content sentiment from both services with stock returns at different time scal es. We show that content from both SeekingAlpha and Stock- Twits provide minimal forward correlation with stock perfor- mance. While the average article provides little value, we find that a subset of "top authors" in SeekingAlpha contribut e content that shows significantly higher correlation with fu ture stock performance. Third, we evaluate the hypothetical performance of simple investment strategies following top authors from both plat- forms. We show that investment strategies based on stock sentiment from top SeekingAlpha authors perform exception- ally well and significantly outperform broader markets. In contrast, strategies relying on StockTwits generally unde rper- form relative to broader markets. In addition, we show that we can identify top authors without historical stock market data, using only user interactions with their articles as a g uide. Fourth, we conduct a large scale survey of SeekingAlpha users and contributors to understand their usage, reliance , and trust in the SeekingAlpha service. Results show that despite seeing potentially intentionally misleading or manipulat ive articles, most users still rely heavily on the site content f or investment advice. Most consider SeekingAlpha unique, and

Includes members:
  Member "comparing and combining sentiment analysis methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modeling spatiotemporal factors associated with sentiment on twitter synthesis and suggestions for improving the identification of localized deviations")
  Member "twitter mood predicts the stock market":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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.SI(Social and Information Networks)