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: "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.

Connected through hyperedge HE-1 (induced by source paper "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"):
  with members:
    - "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
    - "strategies and influence of social bots in a 2017 german state election a case study on twitter"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "holoscope topology and spike aware fraud detection"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "hoaxy a platform for tracking online misinformation"
    - "twitter sentiment around the earnings announcement events"

Connected through hyperedge HE-2 (induced by source paper "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "a meta analysis of state of the art electoral prediction from twitter data"
    - "partisan asymmetries in online political activity"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-3 (induced by source paper "forex trading and twitter spam bots and reputation manipulation"):
  with members:
    - "how to evaluate sentiment classifiers for twitter time ordered data"
    - "twitter sentiment around the earnings announcement events"
    - "multilingual twitter sentiment classification the role of human annotators"
    - "i wish i didn t say that analyzing and predicting deleted messages in twitter"

Connected through hyperedge HE-4 (induced by source paper "twitter sentiment around the earnings announcement events"):
  with members:
    - "multilingual twitter sentiment classification the role of human annotators"
    - "twitter sentiment analysis applied to finance a case study in the retail industry"
    - "sentiment of emojis"
    - "crowds on wall street extracting value from social investing platforms"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (induced by source paper "multilingual twitter sentiment classification the role of human annotators"):
  with members:
    - "emotional dynamics in the age of misinformation"
    - "twitter sentiment analysis lexicon method machine learning method and their combination"
    - "sentiment uncertainty and spam in twitter streams and its implications for general purpose realtime sentiment analysis"
    - "sentiment of emojis"

Connected through hyperedge HE-6 (induced by source paper "cohesion and coalition formation in the european parliament roll call votes and twitter activities"):
  with members:
    - "multilingual twitter sentiment classification the role of human annotators"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-7 (induced by source paper "sentiment of emojis"):
  with members:
    - "emotional dynamics in the age of misinformation"

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.CY(Computers and Society)