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 "software engineering for fairness a case study with hyperparameter optimization"):
  Abstract: We assert that it is the ethical duty of software engineers to strive to reduce software discrimination. This paper discusses how that might be done. This is an important topic since machine learning software is increasingly being used to make decisions that affect people's lives. Potentially, the application of that software will result in fairer decisions because (unlike humans) machine learning software is not biased. However, recent results show that the software within many data mining packages exhibits "group discrimination"; i.e. their decisions are inappropriately affected by "protected attributes"(e.g., race, gender, age, etc.). #R##N#There has been much prior work on validating the fairness of machine-learning models (by recognizing when such software discrimination exists). But after detection, comes mitigation. What steps can ethical software engineers take to reduce discrimination in the software they produce? #R##N#This paper shows that making \textit{fairness} as a goal during hyperparameter optimization can (a) preserve the predictive power of a model learned from a data miner while also (b) generates fairer results. To the best of our knowledge, this is the first application of hyperparameter optimization as a tool for software engineers to generate fairer software.

Includes members:
  Member "finding faster configurations using flash":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning software configuration spaces a systematic literature review")
  Member "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "mitigating unwanted biases with adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic fairness")
  Member "automated directed fairness testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "metamorphic testing for object detection systems")
  Member "hyperparameter optimization for effort estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross dataset design discussion mining")
  Member "a convex framework for fair regression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic fairness")
  Member "fairness testing testing software for discrimination":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "to split or not to split the impact of disparate treatment in classification")
  Member "equality of opportunity in supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "algorithmic fairness")

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)