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: "understanding locally competitive networks"
  Abstract: Recently proposed neural network activation functions such as rectified linear, maxout, and local winner-take-all have allowed for faster and more effective training of deep neural architectures on large and complex datasets. The common trait among these functions is that they implement local competition between small groups of computational units within a layer, so that only part of the network is activated for any given input pattern. In this paper, we attempt to visualize and understand this self-modularization, and suggest a unified explanation for the beneficial properties of such networks. We also show how our insights can be directly useful for efficiently performing retrieval over large datasets using neural networks.

Connected through hyperedge HE-1 (induced by source paper "enhancing adversarial defense by k winners take all"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "hopskipjumpattack a query efficient decision based attack"
    - "theoretically principled trade off between robustness and accuracy"
    - "defensive quantization when efficiency meets robustness"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial training for free"

Connected through hyperedge HE-2 (induced by source paper "what do we understand about convolutional networks"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "oriented response networks"
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"
    - "learning texture manifolds with the periodic spatial gan"
    - "a spatiotemporal oriented energy network for dynamic texture recognition"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-3 (induced by source paper "dynamic channel pruning feature boosting and suppression"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "channel gating neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "more is less a more complicated network with less inference complexity"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-4 (induced by source paper "competition vs concatenation in skip connections of fully convolutional networks"):
  with members:
    - "quicknat segmenting mri neuroanatomy in 20 seconds"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "on the importance of normalisation layers in deep learning with piecewise linear activation units"

Connected through hyperedge HE-5 (induced by source paper "from hard to soft understanding deep network nonlinearities via vector quantization and statistical inference"):
  with members:
    - "deep learning using rectified linear units relu"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "searching for activation functions"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "swish a self gated activation function"
    - "neural photo editing with introspective adversarial networks"
    - "parametric exponential linear unit for deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep convolutional neural network design patterns"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "weighted residuals for very deep networks"
    - "multi residual networks improving the speed and accuracy of residual networks"
    - "convolutional neural fabrics"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-7 (induced by source paper "deep competitive pathway networks"):
  with members:
    - "multi residual networks improving the speed and accuracy of residual networks"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "polynet a pursuit of structural diversity in very deep networks"

Connected through hyperedge HE-8 (induced by source paper "training very deep networks"):
  with members:
    - "grid long short term memory"
    - "binding via reconstruction clustering"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "on the expressive efficiency of sum product networks"
    - "striving for simplicity the all convolutional net"

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.NE(Neural and Evolutionary Computing)