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 "squeezed very deep convolutional neural networks for text classification"):
  Abstract: Embedding artificial intelligence on constrained platforms has become a trend since the growth of embedded systems and mobile devices, experimented in recent years. Although constrained platforms do not have enough processing capabilities to train a sophisticated deep learning model, like convolutional neural networks (CNN), they are already capable of performing inference locally by using a previously trained embedded model. This approach enables numerous advantages such as privacy, response latency, and no real time network dependence. Still, the use of a local CNN model on constrained platforms is restricted by its storage size. Most of the research in CNNs has focused on increasing network depth to improve accuracy. In the text classification area, deep models were proposed with excellent performance but relying on large architectures with thousands of parameters, and consequently, high storage size. We propose to modify the structure of the Very Deep Convolutional Neural Networks (VDCNN) model to reduce its storage size while keeping the model performance. In this paper, we evaluate the impact of Temporal Depthwise Separable Convolutions and Global Average Pooling in the network parameters, storage size, dedicated hardware dependence, and accuracy. The proposed squeezed model (SVDCNN) is between 10x and 20x smaller than the original version, depending on the network depth, maintaining a maximum disk size of 6MB. Regarding accuracy, the network experiences a loss between 0.4% and 1.3% in the accuracy performance while obtains lower latency over non-dedicated hardware and higher inference time ratio compared to the baseline model.

Includes members:
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "do convolutional networks need to be deep for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "word class embeddings for multiclass text classification")
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "depthwise separable convolutions for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical memory decoding for video captioning")
  Member "very deep convolutional networks for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "description based text classification with reinforcement learning")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")
  Member "xception deep learning with depthwise separable convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")

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)