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: "what works better a study of classifying requirements"
  Abstract: Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naive Bayes for the sub-classification of NFRs.

Connected through hyperedge HE-1 (induced by source paper "elica an automated tool for dynamic extraction of requirements relevant information"):
  with members:
    - "dynamic visual analytics for elicitation meetings with elica"
    - "task interruption in software development projects what makes some interruptions more disruptive than others"
    - "let s hear it from retta a requirements elicitation tool for traffic management systems"
    - "task interruptions in requirements engineering reality versus perceptions"

Connected through hyperedge HE-2 (induced by source paper "loud and interactive paper prototyping in requirements elicitation what is it good for"):
  with members:
    - "learn more pay less lessons learned from applying the wizard of oz technique for exploring mobile app requirements"

Connected through hyperedge HE-3 (induced by source paper "learn more pay less lessons learned from applying the wizard of oz technique for exploring mobile app requirements"):
  with members:
    - "let s hear it from retta a requirements elicitation tool for traffic management systems"
    - "factors influencing quality of mobile apps role of mobile app development life cycle"

Connected through hyperedge HE-4 (induced by source paper "a visual narrative path from switching to resuming a requirements engineering task"):
  with members:
    - "choosing requirements for experimentation with user interfaces of requirements modeling tools"
    - "task interruptions in requirements engineering reality versus perceptions"

Connected through hyperedge HE-5 (induced by source paper "task interruptions in requirements engineering reality versus perceptions"):
  with members:
    - "choosing requirements for experimentation with user interfaces of requirements modeling tools"
    - "a visual narrative path from switching to resuming a requirements engineering task"

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)