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 "distill net application specific distillation of deep convolutional neural networks for resource constrained iot platforms"):
  Abstract: Many Internet-of-Things (IoT) applications demand fast and accurate understanding of a few key events in their surrounding environment. Deep Convolutional Neural Networks (CNNs) have emerged as an effective approach to understand speech, images, and similar high dimensional data types. Algorithmic performance of modern CNNs, however, fundamentally relies on learning class-agnostic hierarchical features that only exist in comprehensive training datasets with many classes. As a result, fast inference using CNNs trained on such datasets is prohibitive for most resource-constrained IoT platforms. To bridge this gap, we present a principled and practical methodology for distilling a complex modern CNN that is trained to effectively recognize many different classes of input data into an application-dependent essential core that not only recognizes the few classes of interest to the application accurately, but also runs efficiently on platforms with limited resources. Experimental results confirm that our approach strikes a favorable balance between classification accuracy (application constraint), inference efficiency (platform constraint), and productive development of new applications (business constraint).

Includes members:
  Member "squeezenext hardware aware neural network design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "zeroq a novel zero shot quantization framework")
  Member "outrageously large neural networks the sparsely gated mixture of experts layer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention over parameters for dialogue systems")
  Member "in datacenter performance analysis of a tensor processing unit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lossless compression of deep neural networks")
  Member "condensenet an efficient densenet using learned group convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "cnndroid gpu accelerated execution of trained deep convolutional neural networks on android":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fastva deep learning video analytics through edge processing and npu in mobile")

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)