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: "gabor filter assisted energy efficient fast learning convolutional neural networks"
  Abstract: Convolutional Neural Networks (CNN) are being increasingly used in computer vision for a wide range of classification and recognition problems. However, training these large networks demands high computational time and energy requirements; hence, their energy-efficient implementation is of great interest. In this work, we reduce the training complexity of CNNs by replacing certain weight kernels of a CNN with Gabor filters. The convolutional layers use the Gabor filters as fixed weight kernels, which extracts intrinsic features, with regular trainable weight kernels. This combination creates a balanced system that gives better training performance in terms of energy and time, compared to the standalone CNN (without any Gabor kernels), in exchange for tolerable accuracy degradation. We show that the accuracy degradation can be mitigated by partially training the Gabor kernels, for a small fraction of the total training cycles. We evaluated the proposed approach on 4 benchmark applications. Simple tasks like face detection and character recognition (MNIST and TiCH), were implemented using LeNet architecture. While a more complex task of objet recognition (CIFAR10) was implemented on a state-of-the-art deep CNN (Network in Network) architecture. The proposed approach yields 1.31–1.53× improvement in training energy in comparison to conventional CNN implementation. We also obtain improvement up to 1.4× in training time, up to 2.23× in storage requirements, and up to 2.2× in memory access energy. The accuracy degradation suffered by the approximate implementations is within 0– 3% of the baseline.

Connected through hyperedge HE-1 (induced by source paper "robust gabor networks"):
  with members:
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "kervolutional neural networks"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "the singular values of convolutional layers"
    - "robustness via curvature regularization and vice versa"
    - "structured adversarial attack towards general implementation and better interpretability"
    - "deep watershed transform for instance segmentation"
    - "wide residual networks"

Connected through hyperedge HE-2 (induced by source paper "filter bank regularization of convolutional neural networks"):
  with members:
    - "bridgeout stochastic bridge regularization for deep neural networks"
    - "learning transferable architectures for scalable image recognition"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "gabor convolutional networks"):
  with members:
    - "latent constrained correlation filter"
    - "one two one networks for compression artifacts reduction in remote sensing"
    - "oriented response networks"
    - "deformable convolutional networks"
    - "structured receptive fields in cnns"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "wide residual networks"
    - "local binary convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "tree cnn a hierarchical deep convolutional neural network for incremental learning"):
  with members:
    - "deep convolutional neural networks as generic feature extractors"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "icarl incremental classifier and representation learning"
    - "learning without forgetting"
    - "falcon feature driven selective classification for energy efficient image recognition"
    - "expert gate lifelong learning with a network of experts"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "geometric operator convolutional neural network"):
  with members:
    - "non local neural networks"
    - "deformable convolutional networks"
    - "sgdr stochastic gradient descent with restarts"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "convolutional neural network architectures for matching natural language sentences"
    - "adam a method for stochastic optimization"
    - "deep convolutional filter banks for texture recognition and segmentation"

Connected through hyperedge HE-6 (induced by source paper "incremental learning in deep convolutional neural networks using partial network sharing"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "progressive neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "asp learning to forget with adaptive synaptic plasticity in spiking neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "icarl incremental classifier and representation learning"
    - "identity mappings in deep residual networks"

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)