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 "discovering neural wirings"):
  Abstract: The success of neural networks has driven a shift in focus from feature engineering to architecture engineering. However, successful networks today are constructed using a small and manually defined set of building blocks. Even in methods of neural architecture search (NAS) the network connectivity patterns are largely constrained. In this work we propose a method for discovering neural wirings. We relax the typical notion of layers and instead enable channels to form connections independent of each other. This allows for a much larger space of possible networks. The wiring of our network is not fixed during training -- as we learn the network parameters we also learn the structure itself. Our experiments demonstrate that our learned connectivity outperforms hand engineered and randomly wired networks. By learning the connectivity of MobileNetV1we boost the ImageNet accuracy by 10% at ~41M FLOPs. Moreover, we show that our method generalizes to recurrent and continuous time networks. Our work may also be regarded as unifying core aspects of the neural architecture search problem with sparse neural network learning. As NAS becomes more fine grained, finding a good architecture is akin to finding a sparse subnetwork of the complete graph. Accordingly, DNW provides an effective mechanism for discovering sparse subnetworks of predefined architectures in a single training run. Though we only ever use a small percentage of the weights during the forward pass, we still play the so-called initialization lottery with a combinatorial number of subnetworks. Code and pretrained models are available at this https URL while additional visualizations may be found at this https URL.

Includes members:
  Member "exploring randomly wired neural networks for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "sparse networks from scratch faster training without losing performance":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "soft threshold weight reparameterization for learnable sparsity")
  Member "learning sparse networks using targeted dropout":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural network compression framework for fast model inference")
  Member "luck matters understanding training dynamics of deep relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "over parameterization as a catalyst for better generalization of deep relu network")
  Member "learning implicitly recurrent cnns through parameter sharing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "full stack filters to build minimum viable cnns")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "fbnet hardware aware efficient convnet design via differentiable neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast neural network adaptation via parameter remapping and architecture search")
  Member "shufflenet v2 practical guidelines for efficient cnn architecture design":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "post training piecewise linear quantization for deep neural networks")

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)