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 "dreaming to distill data free knowledge transfer via deepinversion"):
  Abstract: We introduce DeepInversion, a new method for synthesizing images from the image distribution used to train a deep neural network. We 'invert' a trained network (teacher) to synthesize class-conditional input images starting from random noise, without using any additional information about the training dataset. Keeping the teacher fixed, our method optimizes the input while regularizing the distribution of intermediate feature maps using information stored in the batch normalization layers of the teacher. Further, we improve the diversity of synthesized images using Adaptive DeepInversion, which maximizes the Jensen-Shannon divergence between the teacher and student network logits. The resulting synthesized images from networks trained on the CIFAR-10 and ImageNet datasets demonstrate high fidelity and degree of realism, and help enable a new breed of data-free applications - ones that do not require any real images or labeled data. We demonstrate the applicability of our proposed method to three tasks of immense practical importance -- (i) data-free network pruning, (ii) data-free knowledge transfer, and (iii) data-free continual learning.

Includes members:
  Member "hardware guided symbiotic training for compact accurate yet execution efficient lstm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scann synthesis of compact and accurate neural networks")
  Member "adversarial neural network inversion via auxiliary knowledge alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep quaternion features for privacy protection")
  Member "relational knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blurry video frame interpolation")
  Member "variational information distillation for knowledge transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "freeze discriminator a simple baseline for fine tuning gans")
  Member "data free learning of student networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a privacy preserving dnn pruning and mobile acceleration framework")
  Member "importance estimation for neural network pruning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "how good is my gan":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptive density estimation for generative models")

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)