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: "twitter mood predicts the stock market"
  Abstract: Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%. Index Terms—stock market prediction — twitter — mood analysis.

Connected through hyperedge HE-1 (induced by source paper "mining social media data for biomedical signals and health related behavior"):
  with members:
    - "singular value decomposition and neural networks"
    - "anatomy of an online misinformation network"
    - "human sexual cycles are driven by culture and match collective moods"
    - "limited individual attention and online virality of low quality information"
    - "quantified self meets social media sharing of weight updates on twitter"
    - "the rise of social bots"
    - "learning deep structure preserving image text embeddings"
    - "insights from machine learned diet success prediction"

Connected through hyperedge HE-2 (induced by source paper "automatic location type classification from social media posts"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a neural model for user geolocation and lexical dialectology"
    - "neural structural correspondence learning for domain adaptation"
    - "character level convolutional networks for text classification"
    - "skip thought vectors"
    - "improved semantic representations from tree structured long short term memory networks"
    - "named entity recognition with bidirectional lstm cnns"
    - "document embedding with paragraph vectors"

Connected through hyperedge HE-3 (induced by source paper "kryptooracle a real time cryptocurrency price prediction platform using twitter sentiments"):
  with members:
    - "deep learning for time series classification a review"
    - "predicting the effects of news sentiments on the stock market"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-4 (induced by source paper "s apir news based business sentiment index"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "enriching word vectors with subword information"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-5 (induced by source paper "a multi source entity level sentiment corpus for the financial domain the finlin corpus"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"

Connected through hyperedge HE-6 (induced by source paper "information diffusion in social networks friendship paradox based models and statistical inference"):
  with members:
    - "what do your friends think efficient polling methods for networks using friendship paradox"
    - "centrality friendship paradoxes when our friends are more important than us"
    - "conjoining uncooperative societies facilitates evolution of cooperation"
    - "contagions in social networks effects of monophilic contagion friendship paradox and reactive networks"
    - "which friends are more popular than you contact strength and the friendship paradox in social networks"
    - "influence maximization over markovian graphs a stochastic optimization approach"
    - "engagement dynamics and sensitivity analysis of youtube videos"
    - "the majority illusion in social networks"

Connected through hyperedge HE-7 (induced by source paper "leveraging deep graph based text representation for sentiment polarity applications"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "memory enriched big bang big crunch optimization algorithm for data clustering"
    - "fasttext zip compressing text classification models"
    - "w2vlda almost unsupervised system for aspect based sentiment analysis"
    - "node2vec scalable feature learning for networks"
    - "relevant based structure learning for feature selection"
    - "deep learning in neural networks"
    - "line large scale information network embedding"

Connected through hyperedge HE-8 (induced by source paper "deep sentiment analysis using a graph based text representation"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "memory enriched big bang big crunch optimization algorithm for data clustering"
    - "fasttext zip compressing text classification models"
    - "w2vlda almost unsupervised system for aspect based sentiment analysis"
    - "node2vec scalable feature learning for networks"
    - "relevant based structure learning for feature selection"
    - "deep learning in neural networks"
    - "line large scale information network embedding"

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.CE(Computational Engineering, Finance, and Science)