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: "volatility prediction using financial disclosures sentiments with word embedding based ir models"
  Abstract: Volatility prediction--an essential concept in financial markets--has recently been addressed using sentiment analysis methods. We investigate the sentiment of annual disclosures of companies in stock markets to forecast volatility. We specifically explore the use of recent Information Retrieval (IR) term weighting models that are effectively extended by related terms using word embeddings. In parallel to textual information, factual market data have been widely used as the mainstream approach to forecast market risk. We therefore study different fusion methods to combine text and market data resources. Our word embedding-based approach significantly outperforms state-of-the-art methods. In addition, we investigate the characteristics of the reports of the companies in different financial sectors.

Connected through hyperedge HE-1 (induced by source paper "modeling financial analysts decision making via the pragmatics and semantics of earnings calls"):
  with members:
    - "attentive interaction model modeling changes in view in argumentation"
    - "asking too much the rhetorical role of questions in political discourse"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "relnet end to end modeling of entities relations"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "leverage financial news to predict stock price movements using word embeddings and deep neural networks"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "deep learning for sentiment analysis a survey"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "tensor fusion network for multimodal sentiment analysis"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "deep memory networks for attitude identification"
    - "interactive attention networks for aspect level sentiment classification"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "a hierarchical model of reviews for aspect based sentiment analysis"

Connected through hyperedge HE-3 (induced by source paper "toward incorporation of relevant documents in word2vec"):
  with members:
    - "neural ranking models with weak supervision"
    - "relevance based word embedding"
    - "query expansion with locally trained word embeddings"
    - "a dual embedding space model for document ranking"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "sparse overcomplete word vector representations"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"

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.IR(Information Retrieval)