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: "on learning meaningful assert statements for unit test cases"
  Abstract: Software testing is an essential part of the software lifecycle andrequires a substantial amount of time and effort. It has been esti-mated that software developers spend close to 50% of their time ontesting the code they write. For these reasons, a long standing goalwithin the research community is to (partially) automate softwaretesting. While several techniques and tools have been proposedto automatically generate test methods, recent work has criticizedthe quality and usefulness of the assert statements they generate.Therefore, we employ a Neural Machine Translation (NMT) basedapproach calledAtlas(AuTomaticLearning ofAssertStatements)to automatically generate meaningful assert statements for testmethods. Given a test method and a focal method (i.e.,the mainmethod under test),Atlascan predict a meaningful assert state-ment to assess the correctness of the focal method. We appliedAtlasto thousands of test methods from GitHub projects and itwas able to predict the exact assert statement manually writtenby developers in 31% of the cases when only considering the top-1 predicted assert. When considering the top-5 predicted assertstatements,Atlasis able to predict exact matches in 50% of thecases. These promising results hint to the potential usefulness ofour approach as (i) a complement to automatic test case generationtechniques, and (ii) a code completion support for developers, whocan benefit from the recommended assert statements while writingtest code.

Connected through hyperedge HE-1 (induced by source paper "typewriter neural type prediction with search based validation"):
  with members:
    - "big code big vocabulary open vocabulary models for source code"
    - "modeling vocabulary for big code machine learning"
    - "on learning meaningful code changes via neural machine translation"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "getafix learning to fix bugs automatically"
    - "learning to represent edits"
    - "neural networks for modeling source code edits"
    - "learning to repair software vulnerabilities with generative adversarial networks"

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)