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: "deep convolutional neural networks as generic feature extractors"
  Abstract: Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve convincing results and are currently the state-of-the-art approach for this task. However, the long time needed to train such deep networks is a major drawback. We tackled this problem by reusing a previously trained network. For this purpose, we first trained a deep convolutional network on the ILSVRC2012 dataset. We then maintained the learned convolution kernels and only retrained the classification part on different datasets. Using this approach, we achieved an accuracy of 67.68 % on CIFAR-100, compared to the previous state-of-the-art result of 65.43 %. Furthermore, our findings indicate that convolutional networks are able to learn generic feature extractors that can be used for different tasks.

Connected through hyperedge HE-1 (induced by source paper "tree cnn a hierarchical deep convolutional neural network for incremental learning"):
  with members:
    - "gabor filter assisted energy efficient fast learning convolutional neural networks"
    - "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-2 (induced by source paper "tree cnn a deep convolutional neural network for lifelong learning"):
  with members:
    - "progressive neural networks"
    - "incremental learning in deep convolutional neural networks using partial network sharing"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "learning without forgetting"
    - "expert gate lifelong learning with a network of experts"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-3 (induced by source paper "one class feature learning using intra class splitting"):
  with members:
    - "learning deep features for one class classification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "tensorflow a system for large scale machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "deep learning with convolutional neural networks for eeg decoding and visualization"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "learning robust features using deep learning for automatic seizure detection"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "inverting visual representations with convolutional networks"
    - "deep learning in neural networks"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"

Connected through hyperedge HE-5 (induced by source paper "ternary neural networks for resource efficient ai applications"):
  with members:
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "bitwise neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-6 (induced by source paper "recursive autoconvolution for unsupervised learning of convolutional neural networks"):
  with members:
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "unsupervised learning of visual representations using videos"
    - "convolutional clustering for unsupervised learning"
    - "semi supervised learning with ladder networks"
    - "winner take all autoencoders"
    - "committees of deep feedforward networks trained with few data"
    - "convolutional kernel networks"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-7 (induced by source paper "comparing time and frequency domain for audio event recognition using deep learning"):
  with members:
    - "going deeper with convolutions"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "theano new features and speed improvements"

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.CV(Computer Vision and Pattern Recognition)