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: "a new statistical approach for comparing algorithms for lexicon based sentiment analysis"
  Abstract: Lexicon based sentiment analysis usually relies on the identification of various words to which a numerical value corresponding to sentiment can be assigned. In principle, classifiers can be obtained from these algorithms by comparison with human annotation, which is considered the gold standard. In practise this is difficult in languages such as Portuguese where there is a paucity of human annotated texts. Thus in order to compare algorithms, a next best step is to directly compare different algorithms with each other without referring to human annotation. In this paper we develop methods for a statistical comparison of algorithms which does not rely on human annotation or on known class labels. We will motivate the use of marginal homogeneity tests, as well as log linear models within the framework of maximum likelihood estimation We will also show how some uncertainties present in lexicon based sentiment analysis may be similar to those which occur in human annotated tweets. We will also show how the variability in the output of different algorithms is lexicon dependent, and quantify this variability in the output within the framework of log linear models.

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)