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 "exploring bias in gan based data augmentation for small samples"):
  Abstract: For machine learning task, lacking sufficient samples mean the trained model has low confidence to approach the ground truth function. Until recently, after the generative adversarial networks (GAN) had been proposed, we see the hope of small samples data augmentation (DA) with realistic fake data, and many works validated the viability of GAN-based DA. Although most of the works pointed out higher accuracy can be achieved using GAN-based DA, some researchers stressed that the fake data generated from GAN has inherent bias, and in this paper, we explored when the bias is so low that it cannot hurt the performance, we set experiments to depict the bias in different GAN-based DA setting, and from the results, we design a pipeline to inspect specific dataset is efficiently-augmentable with GAN-based DA or not. And finally, depending on our trial to reduce the bias, we proposed some advice to mitigate bias in GAN-based DA application.

Includes members:
  Member "gansfer learning combining labelled and unlabelled data for gan based data augmentation":
    also connected through other hyperedges:
      (none)
  Member "imagining an engineer on gan based data augmentation perpetuating biases":
    also connected through other hyperedges:
      (none)
  Member "t cgan conditional generative adversarial network for data augmentation in noisy time series with irregular sampling":
    also connected through other hyperedges:
      (none)
  Member "dada deep adversarial data augmentation for extremely low data regime classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data augmentation with mobius transformations")
  Member "code switching sentence generation by generative adversarial networks and its application to data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained output embeddings for end to end code switching speech recognition with only monolingual data")
  Member "pixel level data augmentation for semantic image segmentation using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self ensembling with gan based data augmentation for domain adaptation in semantic segmentation")
  Member "prostategan mitigating data bias via prostate diffusion imaging synthesis with generative adversarial networks":
    also connected through other hyperedges:
      (none)
  Member "doping generative data augmentation for unsupervised anomaly detection with gan":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rcc dual gan an efficient approach for outlier detection with few identified anomalies")

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)