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: "from maxout to channel out encoding information on sparse pathways"
  Abstract: Motivated by an important insight from neural science, we propose a new framework for understanding the success of the recently proposed "maxout" networks. The framework is based on encoding information on sparse pathways and recognizing the correct pathway at inference time. Elaborating further on this insight, we propose a novel deep network architecture, called "channel-out" network, which takes a much better advantage of sparse pathway encoding. In channel-out networks, pathways are not only formed a posteriori, but they are also actively selected according to the inference outputs from the lower layers. From a mathematical perspective, channel-out networks can represent a wider class of piece-wise continuous functions, thereby endowing the network with more expressive power than that of maxout networks. We test our channel-out networks on several well-known image classification benchmarks, setting new state-of-the-art performance on CIFAR-100 and STL-10, which represent some of the "harder" image classification benchmarks.

Connected through hyperedge HE-1 (induced by source paper "input fast forwarding for better deep learning"):
  with members:
    - "an image dataset of text patches in everyday scenes"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "holistically nested edge detection"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"
    - "cudnn efficient primitives for deep learning"
    - "stochastic pooling for regularization of deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "multi bias non linear activation in deep neural networks"):
  with members:
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "empirical evaluation of rectified activations in convolutional network"
    - "deep networks with internal selective attention through feedback connections"
    - "learning activation functions to improve deep neural networks"
    - "network in network"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-3 (induced by source paper "batch normalized maxout network in network"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the importance of normalisation layers in deep learning with piecewise linear activation units"
    - "matconvnet convolutional neural networks for matlab"
    - "visualizing and understanding convolutional networks"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-4 (induced by source paper "learning activation functions to improve deep neural networks"):
  with members:
    - "deep networks with internal selective attention through feedback connections"
    - "caffe convolutional architecture for fast feature embedding"
    - "network in network"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)