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 "degan data enriching gan for retrieving representative samples from a trained classifier"):
  Abstract: In this era of digital information explosion, an abundance of data from numerous modalities is being generated as well as archived everyday. However, most problems associated with training Deep Neural Networks still revolve around lack of data that is rich enough for a given task. Data is required not only for training an initial model, but also for future learning tasks such as Model Compression and Incremental Learning. A diverse dataset may be used for training an initial model, but it may not be feasible to store it throughout the product life cycle due to data privacy issues or memory constraints. We propose to bridge the gap between the abundance of available data and lack of relevant data, for the future learning tasks of a given trained network. We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples from a trained classifier, using a novel Data-enriching GAN (DeGAN) framework. We demonstrate that data from a related domain can be leveraged to achieve state-of-the-art performance for the tasks of Data-free Knowledge Distillation and Incremental Learning on benchmark datasets. We further demonstrate that our proposed framework can enrich any data, even from unrelated domains, to make it more useful for the future learning tasks of a given network.

Includes members:
  Member "learning from higher layer feature visualizations":
    also connected through other hyperedges:
      (none)
  Member "zero shot knowledge distillation in deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative low bitwidth data free quantization")
  Member "ask acquire and attack data free uap generation using class impressions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "defending against universal attacks through selective feature regeneration")
  Member "few sample knowledge distillation for efficient network compression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "knowledge distillation from few samples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "end to end incremental learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "incremental object detection via meta learning")
  Member "domain adaptation for visual applications a comprehensive survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "statistics of deep generated images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pros and cons of gan evaluation measures")

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)