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: "mat a simple yet strong baseline for identifying self admitted technical debt"
  Abstract: In the process of software evolution, developers often sacrifice the long-term code quality to satisfy the short-term goals due to specific reasons, which is called technical debt. In particular, self-admitted technical debt (SATD) refers to those that were intentionally introduced and remarked by code comments. Those technical debts reduce the quality of software and increase the cost of subsequent software maintenance. Therefore, it is necessary to find out and resolve these debts in time. Recently, many approaches have been proposed to identify SATD. However, those approaches either have a low accuracy or are complex to implementation in practice. In this paper, we propose a simple unsupervised baseline approach that fuzzily matches task annotation tags (MAT) to identify SATD. MAT does not need any training data to build a prediction model. Instead, MAT only examines whether any of four task tags (i.e. TODO, FIXME, XXX, and HACK) appears in the comments of a target project to identify SATD. In this sense, MAT is a natural baseline approach, which has a good understandability, in SATD identification. In order to evaluate the usefulness of MAT, we use 10 open-source projects to conduct the experiment. The experimental results reveal that MAT has a surprisingly excellent performance for SATD identification compared with the state-of-the-art approaches. As such, we suggest that, in the future SATD identification studies, MAT should be considered as an easy-to-implement baseline to which any new approach should be compared against to demonstrate its usefulness.

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.SE(Software Engineering)