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 "the effects of twitter sentiment on stock price returns"):
  Abstract: Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known “event study” from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the “event study” methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1–2%), but the dependence is statistically significant for several days after the events.

Includes members:
  Member "emotional dynamics in the age of misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities")
  Member "twitter sentiment analysis applied to finance a case study in the retail industry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "twitter sentiment around the earnings announcement events")
  Member "twitter sentiment analysis lexicon method machine learning method and their combination":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a new statistical approach for comparing algorithms for lexicon based sentiment analysis")
  Member "cohesiveness in financial news and its relation to market volatility":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sensing social media signals for cryptocurrency news")
  Member "where in the world are you geolocation and language identification in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a hierarchical location prediction neural network for twitter user geolocation")
  Member "the royal birth of 2013 analysing and visualising public sentiment in the uk using twitter":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a machine learning analysis of twitter sentiment to the sandy hook shootings")
  Member "dynamical classes of collective attention in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "language in our time an empirical analysis of hashtags")
  Member "twitter mood predicts the stock market":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mining social media data for biomedical signals and health related behavior")

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.CY(Computers and Society)