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 "expandnets exploiting linear redundancy to train small networks"):
  Abstract: While very deep networks can achieve great performance, they are ill-suited to applications in resource-constrained environments. Knowledge transfer, which leverages a deep teacher network to train a given small network, has emerged as one of the most popular strategies to address this problem. In this paper, we introduce an alternative approach to training a given small network, based on the intuition that parameter redundancy facilitates learning. We propose to expand each linear layer of a small network into multiple linear layers, without adding any nonlinearity. As such, the resulting expanded network can be compressed back to the small one algebraically, but, as evidenced by our experiments, consistently outperforms training the small network from scratch. This strategy is orthogonal to knowledge transfer. We therefore further show on several standard benchmarks that, for any knowledge transfer technique, using our expanded network as student systematically improves over using the small network.

Includes members:
  Member "on the optimization of deep networks implicit acceleration by overparameterization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "learning and generalization in overparameterized neural networks going beyond two layers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "yolo lite a real time object detection algorithm optimized for non gpu computers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "penet object detection using points estimation in aerial images")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "learning deep representations with probabilistic knowledge transfer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "supermix supervising the mixing data augmentation")
  Member "deep linear neural networks with arbitrary loss all local minima are global":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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.CV(Computer Vision and Pattern Recognition)