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 community s perspective on the status and future of peer review in software engineering"
  Abstract: Context: Pre-publication peer review of scientific articles is considered a key element of the research process in software engineering, yet it is often perceived as not to work fully well. #R##N#Objective: We aim at understanding the perceptions of and attitudes towards peer review of authors and reviewers at one of software engineering's most prestigious venues, the International Conference on Software Engineering (ICSE). #R##N#Method: We invited 932 ICSE 2014/15/16 authors and reviewers to participate in a survey with 10 closed and 9 open questions. #R##N#Results: We present a multitude of results, such as: Respondents perceive only one third of all reviews to be good, yet one third as useless or misleading; they propose double-blind or zero-blind reviewing regimes for improvement; they would like to see showable proofs of (good) reviewing work be introduced; attitude change trends are weak. #R##N#Conclusion: The perception of the current state of software engineering peer review is fairly negative. Also, we found hardly any trend that suggests reviewing will improve by itself over time; the community will have to make explicit efforts. Fortunately, our (mostly senior) respondents appear more open for trying different peer reviewing regimes than we had expected.

Connected through hyperedge HE-1 (induced by source paper "responsible and representative multimodal data acquisition and analysis on auditability benchmarking confidence data reliance explainability"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "gatekeeping algorithms with human ethical bias the ethics of algorithms in archives libraries and society"
    - "multimodal machine learning a survey and taxonomy"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "a survey on measuring indirect discrimination in machine learning"

Connected through hyperedge HE-2 (induced by source paper "open science in software engineering"):
  with members:
    - "empirical software engineering from discipline to interdiscipline"
    - "a framework for systematic analysis of open access journals and its application in software engineering and information systems"
    - "it was twenty years ago today"

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)