human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "knowledge distillation from few samples"
  Abstract: Current knowledge distillation methods require full training data to distill knowledge from a large "teacher" network to a compact "student" network by matching certain statistics between "teacher" and "student" such as softmax outputs and feature responses. This is not only time-consuming but also inconsistent with human cognition in which children can learn knowledge from adults with few examples. This paper proposes a novel and simple method for knowledge distillation from few samples. Taking the assumption that both "teacher" and "student" have the same feature map sizes at each corresponding block, we add a 1x1 conv-layer at the end of each block in the student-net, and align the block-level outputs between "teacher" and "student" by estimating the parameters of the added layer with limited samples. We prove that the added layer can be absorbed/merged into the previous conv-layer to formulate a new conv-layer with the same size of parameters and computation cost as the previous one. Experiments verify that the proposed method is very efficient and effective to distill knowledge from teacher-net to student-net constructing in different ways on various datasets.

Connected through hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "design automation for efficient deep learning computing"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "semantic image synthesis with spatially adaptive normalization"
    - "co evolutionary compression for unpaired image translation"
    - "compressing gans using knowledge distillation"
    - "video to video synthesis"

Connected through hyperedge HE-2 (induced by source paper "zero shot knowledge transfer via adversarial belief matching"):
  with members:
    - "dataset distillation"
    - "variational information distillation for knowledge transfer"
    - "zero shot knowledge distillation in deep networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "data free knowledge distillation for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "few shot network compression via cross distillation"):
  with members:
    - "structured pruning of recurrent neural networks through neuron selection"
    - "rtn reparameterized ternary network"
    - "zero shot knowledge distillation in deep networks"
    - "scalable methods for 8 bit training of neural networks"
    - "proxquant quantized neural networks via proximal operators"
    - "few sample knowledge distillation for efficient network compression"
    - "rethinking the value of network pruning"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-4 (induced by source paper "positive unlabeled compression on the cloud"):
  with members:
    - "dafl data free learning of student networks"
    - "full stack filters to build minimum viable cnns"
    - "revisiting sample selection approach to positive unlabeled learning turning unlabeled data into positive rather than negative"
    - "data free learning of student networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimnets an exploration of deep model compression and acceleration"
    - "few sample knowledge distillation for efficient network compression"
    - "squeeze and excitation networks"

Connected through hyperedge HE-5 (induced by source paper "degan data enriching gan for retrieving representative samples from a trained classifier"):
  with members:
    - "learning from higher layer feature visualizations"
    - "zero shot knowledge distillation in deep networks"
    - "ask acquire and attack data free uap generation using class impressions"
    - "few sample knowledge distillation for efficient network compression"
    - "end to end incremental learning"
    - "domain adaptation for visual applications a comprehensive survey"
    - "statistics of deep generated images"
    - "continual learning with deep generative replay"

Question: Please tell me which class the center node 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)