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: "modularized morphing of neural networks"
  Abstract: In this work we study the problem of network morphism, an effective learning scheme to morph a well-trained neural network to a new one with the network function completely preserved. Different from existing work where basic morphing types on the layer level were addressed, we target at the central problem of network morphism at a higher level, i.e., how a convolutional layer can be morphed into an arbitrary module of a neural network. To simplify the representation of a network, we abstract a module as a graph with blobs as vertices and convolutional layers as edges, based on which the morphing process is able to be formulated as a graph transformation problem. Two atomic morphing operations are introduced to compose the graphs, based on which modules are classified into two families, i.e., simple morphable modules and complex modules. We present practical morphing solutions for both of these two families, and prove that any reasonable module can be morphed from a single convolutional layer. Extensive experiments have been conducted based on the state-of-the-art ResNet on benchmark datasets, and the effectiveness of the proposed solution has been verified.

Connected through hyperedge HE-1 (induced by source paper "neural inheritance relation guided one shot layer assignment search"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "exploring randomly wired neural networks for image recognition"
    - "autogrow automatic layer growing in deep convolutional networks"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autoslim towards one shot architecture search for channel numbers"
    - "mfas multimodal fusion architecture search"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "autogrow automatic layer growing in deep convolutional networks"):
  with members:
    - "cgap continuous growth and pruning for efficient deep learning"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "evolving deep neural networks"
    - "progressive neural architecture search"
    - "learning efficient convolutional networks through network slimming"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-3 (induced by source paper "a review of meta reinforcement learning for deep neural networks architecture search"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "efficient progressive neural architecture search"
    - "speeding up the hyperparameter optimization of deep convolutional neural networks"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural architecture search"
    - "efficient architecture search by network transformation"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-4 (induced by source paper "rapid training of very large ensembles of diverse neural networks"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep mutual learning"
    - "snapshot ensembles train 1 get m for free"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "less is more a comprehensive framework for the number of components of ensemble classifiers"
    - "deep networks with stochastic depth"
    - "densely connected convolutional networks"
    - "learning without forgetting"

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)