human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "studying software engineering patterns for designing machine learning systems"):
  Abstract: Machine-learning (ML) techniques have become popular in the recent years. ML techniques rely on mathematics and on software engineering. Researchers and practitioners studying best practices for designing ML application systems and software to address the software complexity and quality of ML techniques. Such design practices are often formalized as architecture patterns and design patterns by encapsulating reusable solutions to commonly occurring problems within given contexts. However, to the best of our knowledge, there has been no work collecting, classifying, and discussing these software-engineering (SE) design patterns for ML techniques systematically. Thus, we set out to collect good/bad SE design patterns for ML techniques to provide developers with a comprehensive and ordered classification of such patterns. We report here preliminary results of a systematic-literature review (SLR) of good/bad design patterns for ML.

Includes members:
  Member "landscape of iot patterns":
    also connected through other hyperedges:
      (none)
  Member "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "model assertions for monitoring and improving ml model")
  Member "machine learning software engineering in practice an industrial case study":
    also connected through other hyperedges:
      (none)
  Member "deep convolutional neural network design patterns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "assisting human experts in the interpretation of their visual process a case study on assessing copper surface adhesive potency")

Question: Please tell me which class the source paper 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)