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: "an empirical evaluation of text representation schemes on multilingual social web to filter the textual aggression"
  Abstract: This paper attempt to study the effectiveness of text representation schemes on two tasks namely: User Aggression and Fact Detection from the social media contents. In User Aggression detection, The aim is to identify the level of aggression from the contents generated in the Social media and written in the English, Devanagari Hindi and Romanized Hindi. Aggression levels are categorized into three predefined classes namely: `Non-aggressive`, `Overtly Aggressive`, and `Covertly Aggressive`. During the disaster-related incident, Social media like, Twitter is flooded with millions of posts. In such emergency situations, identification of factual posts is important for organizations involved in the relief operation. We anticipated this problem as a combination of classification and Ranking problem. This paper presents a comparison of various text representation scheme based on BoW techniques, distributed word/sentence representation, transfer learning on classifiers. Weighted $F_1$ score is used as a primary evaluation metric. Results show that text representation using BoW performs better than word embedding on machine learning classifiers. While pre-trained Word embedding techniques perform better on classifiers based on deep neural net. Recent transfer learning model like ELMO, ULMFiT are fine-tuned for the Aggression classification task. However, results are not at par with pre-trained word embedding model. Overall, word embedding using fastText produce best weighted $F_1$-score than Word2Vec and Glove. Results are further improved using pre-trained vector model. Statistical significance tests are employed to ensure the significance of the classification results. In the case of lexically different test Dataset, other than training Dataset, deep neural models are more robust and perform substantially better than machine learning classifiers.

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)