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: "sentiment analysis how to derive prior polarities from sentiwordnet"
  Abstract: Assigning a positive or negative score to a word out of context (i.e. a word's prior polarity) is a challenging task for sentiment analysis. In the literature, various approaches based on SentiWordNet have been proposed. In this paper, we compare the most often used techniques together with newly proposed ones and incorporate all of them in a learning framework to see whether blending them can further improve the estimation of prior polarity scores. Using two different versions of SentiWordNet and testing regression and classification models across tasks and datasets, our learning approach consistently outperforms the single metrics, providing a new state-of-the-art approach in computing words' prior polarity for sentiment analysis. We conclude our investigation showing interesting biases in calculated prior polarity scores when word Part of Speech and annotator gender are considered.

Connected through hyperedge HE-1 (induced by source paper "sentilr linguistic knowledge enhanced language representation for sentiment analysis"):
  with members:
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "grammatical analysis of pretrained sentence encoders with acceptability judgments"
    - "mass masked sequence to sequence pre training for language generation"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "story ending prediction by transferable bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"

Connected through hyperedge HE-2 (induced by source paper "using stock prices as ground truth in sentiment analysis to generate profitable trading signals"):
  with members:
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-3 (induced by source paper "sentiwords deriving a high precision and high coverage lexicon for sentiment analysis"):
  with members:
    - "onts optima news translation system"
    - "crowdsourcing a word emotion association lexicon"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"
    - "more accurate tests for the statistical significance of result differences"

Connected through hyperedge HE-4 (induced by source paper "depechemood a lexicon for emotion analysis from crowd annotated news"):
  with members:
    - "onts optima news translation system"

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.CL(Computation and Language)