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 "neural styling for interpretable fair representations"):
  Abstract: We observe a rapid increase in machine learning models for learning data representations that remove the semantics of protected characteristics, and are therefore able to mitigate unfair prediction outcomes. This is indeed a positive proliferation. All available models however learn latent embeddings, therefore the produced representations do not have the semantic meaning of the input. Our aim here is to learn fair representations that are directly interpretable in the original input domain. We cast this problem as a data-to-data translation; to learn a mapping from data in a source domain to a target domain such that data in the target domain enforces fairness definitions, such as statistical parity or equality of opportunity. Unavailability of fair data in the target domain is the crux of the problem. This paper provides the first approach to learn a highly unconstrained mapping from source to target by maximizing (conditional) dependence of residuals - the difference between data and its translated version - and protected characteristics. The usage of residual statistics ensures that our generated fair data should only be an adjustment of the input data, and this adjustment should reveal the main difference between protected characteristic groups. When applied to CelebA face image dataset with gender as protected characteristic, our model enforces equality of opportunity by adjusting eyes and lips regions. In Adult income dataset, also with gender as protected characteristic, our model achieves equality of opportunity by, among others, obfuscating wife and husband relationship. Visualizing those systematic changes will allow us to scrutinize the interplay of fairness criterion, chosen protected characteristics, and the prediction performance.

Includes members:
  Member "diversity in faces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "mitigating unwanted biases with adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithmic fairness")
  Member "learning adversarially fair and transferable representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic fairness")
  Member "data decisions and theoretical implications when adversarially learning fair representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic fairness")
  Member "counterfactual explanations without opening the black box automated decisions and the gdpr":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep domain adversarial image generation for domain generalisation")
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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.LG(Machine Learning)