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: "sentence level sentiment analysis of financial news using distributed text representations and multi instance learning"
  Abstract: Researchers and financial professionals require robust computerized tools that allow users to rapidly operationalize and assess the semantic textual content in financial news. However, existing methods commonly work at the document-level while deeper insights into the actual structure and the sentiment of individual sentences remain blurred. As a result, investors are required to apply the utmost attention and detailed, domain-specific knowledge in order to assess the information on a fine-grained basis. To facilitate this manual process, this paper proposes the use of distributed text representations and multi-instance learning to transfer information from the document-level to the sentence-level. Compared to alternative approaches, this method features superior predictive performance while preserving context and interpretability. Our analysis of a manually-labeled dataset yields a predictive accuracy of up to 69.90%, exceeding the performance of alternative approaches by at least 3.80 percentage points. Accordingly, this study not only benefits investors with regard to their financial decision-making, but also helps companies to communicate their messages as intended.

Connected through hyperedge HE-1 (induced by source paper "finbert financial sentiment analysis with pre trained language models"):
  with members:
    - "how to fine tune bert for text classification"
    - "deep learning for sentiment analysis a survey"
    - "practical text classification with large pre trained language models"
    - "deep contextualized word representations"
    - "financial aspect based sentiment analysis using deep representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-2 (induced by source paper "the longer the better the interplay between review length and line of argumentation in online consumer reviews"):
  with members:
    - "understanding the role of two sided argumentation in online consumer reviews a language based perspective"
    - "statistical inferences for polarity identification in natural language"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "distributed representations of sentences and documents"

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)