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: "a novel feature extraction method for scene recognition based on centered convolutional restricted boltzmann machines"
  Abstract: Scene recognition is an important research topic in computer vision, while feature extraction is a key step of object recognition. Although classical Restricted Boltzmann machines (RBM) can efficiently represent complicated data, it is hard to handle large images due to its complexity in computation. In this paper, a novel feature extraction method, named Centered Convolutional Restricted Boltzmann Machines (CCRBM), is proposed for scene recognition. The proposed model is an improved Convolutional Restricted Boltzmann Machines (CRBM) by introducing centered factors in its learning strategy to reduce the source of instabilities. First, the visible units of the network are redefined using centered factors. Then, the hidden units are learned with a modified energy function by utilizing a distribution function, and the visible units are reconstructed using the learned hidden units. In order to achieve better generative ability, the Centered Convolutional Deep Belief Networks (CCDBN) is trained in a greedy layer-wise way. Finally, a softmax regression is incorporated for scene recognition. Extensive experimental evaluations using natural scenes, MIT-indoor scenes, and Caltech 101 datasets show that the proposed approach performs better than other counterparts in terms of stability, generalization, and discrimination. The CCDBN model is more suitable for natural scene image recognition by virtue of convolutional property.

Connected through hyperedge HE-1 (induced by source paper "plug and play rescaling based crowd counting in static images"):
  with members:
    - "zoomcount a zooming mechanism for crowd counting in static images"
    - "counting with focus for free"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "object detection with convolutional neural networks"
    - "mdfn multi scale deep feature learning network for object detection"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "from open set to closed set counting objects by spatial divide and conquer"

Connected through hyperedge HE-2 (induced by source paper "mdfn multi scale deep feature learning network for object detection"):
  with members:
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "yolov3 an incremental improvement"
    - "a 2 nets double attention networks"
    - "learning depth from single images with deep neural network embedding focal length"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "adversarially approximated autoencoder for image generation and manipulation"):
  with members:
    - "deep visual attention prediction"
    - "neural autoregressive flows"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "video salient object detection via fully convolutional networks"
    - "learning depth from single images with deep neural network embedding focal length"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "adversarial symmetric variational autoencoder"

Connected through hyperedge HE-4 (induced by source paper "object detection with convolutional neural networks"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "vision meets drones a challenge"
    - "deep learning for generic object detection a survey"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "yolov3 an incremental improvement"
    - "quantization mimic towards very tiny cnn for object detection"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"

Connected through hyperedge HE-5 (induced by source paper "boosting occluded image classification via subspace decomposition based estimation of deep features"):
  with members:
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "facelet bank for fast portrait manipulation"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "learning depth from single images with deep neural network embedding focal length"
    - "generative image inpainting with contextual attention"
    - "network dissection quantifying interpretability of deep visual representations"
    - "cloud based deep learning of big eeg data for epileptic seizure prediction"
    - "perceptual generative adversarial networks for small object detection"

Connected through hyperedge HE-6 (induced by source paper "self learning local supervision encoding framework to constrict and disperse feature distribution for clustering"):
  with members:
    - "restricted boltzmann machines with gaussian visible units guided by pairwise constraints"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "deep transductive semi supervised maximum margin clustering"
    - "a generative restricted boltzmann machine based method for high dimensional motion data modeling"
    - "learning structured ordinal measures for video based face recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "restricted boltzmann machines with gaussian visible units guided by pairwise constraints"):
  with members:
    - "gaussian binary restricted boltzmann machines for modeling natural image statistics"
    - "estimating 3d trajectories from 2d projections via disjunctive factored four way conditional restricted boltzmann machines"
    - "theta rbm unfactored gated restricted boltzmann machine for rotation invariant representations"
    - "deep transductive semi supervised maximum margin clustering"
    - "a generative restricted boltzmann machine based method for high dimensional motion data modeling"
    - "speech recognition with deep recurrent neural networks"
    - "modeling documents with deep boltzmann machines"
    - "conditional restricted boltzmann machines for structured output prediction"

Connected through hyperedge HE-8 (induced by source paper "exemplar based deep discriminative and shareable feature learning for scene image classification"):
  with members:
    - "a spatial layout and scale invariant feature representation for indoor scene classification"
    - "a discriminative representation of convolutional features for indoor scene recognition"
    - "inverting visual representations with convolutional networks"
    - "natural scene recognition based on superpixels and deep boltzmann machines"
    - "max margin based discriminative feature learning"
    - "unsupervised feature learning with c svddnet"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "discriminative unsupervised feature learning with exemplar convolutional neural 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.CV(Computer Vision and Pattern Recognition)