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: "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
  Abstract: Object detection in challenging situations such as scale variation, occlusion, and truncation depends not only on feature details but also on contextual information. Most previous networks emphasize too much on detailed feature extraction through deeper and wider networks, which may enhance the accuracy of object detection to certain extent. However, the feature details are easily being changed or washed out after passing through complicated filtering structures. To better handle these challenges, the paper proposes a novel framework, multi-scale, deep inception convolutional neural network (MDCN), which focuses on wider and broader object regions by activating feature maps produced in the deep part of the network. Instead of incepting inner layers in the shallow part of the network, multi-scale inceptions are introduced in the deep layers. The proposed framework integrates the contextual information into the learning process through a single-shot network structure. It is computational efficient and avoids the hard training problem of previous macro feature extraction network designed for shallow layers. Extensive experiments demonstrate the effectiveness and superior performance of MDCN over the state-of-the-art models.

Connected through hyperedge HE-1 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters"):
  with members:
    - "orthogonal deep neural networks"
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "mdfn multi scale deep feature learning network for object detection"
    - "towards learning affine invariant representations via data efficient cnns"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "interpretable convolutional neural networks via feedforward design"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"

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"
    - "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"
    - "blitznet a real time deep network for scene understanding"

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"
    - "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"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"

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"
    - "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"
    - "learning depth from single images with deep neural network embedding focal length"

Connected through hyperedge HE-5 (induced by source paper "adaptively denoising proposal collection for weakly supervised object localization"):
  with members:
    - "stacked wasserstein autoencoder"
    - "direct visual inertial odometry with semi dense mapping"
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "towards learning affine invariant representations via data efficient cnns"
    - "learning depth from single images with deep neural network embedding focal length"
    - "soft nms improving object detection with one line of code"
    - "how hard can it be estimating the difficulty of visual search in an image"

Connected through hyperedge HE-6 (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"
    - "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"
    - "cvae gan fine grained image generation through asymmetric training"

Connected through hyperedge HE-7 (induced by source paper "unsupervised deep feature transfer for low resolution image classification"):
  with members:
    - "stacked wasserstein autoencoder"
    - "adaptively denoising proposal collection for weakly supervised object localization"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "deep clustering for unsupervised learning of visual features"
    - "learning depth from single images with deep neural network embedding focal length"
    - "invariant information clustering for unsupervised image classification and segmentation"
    - "vision based real time aerial object localization and tracking for uav sensing system"

Connected through hyperedge HE-8 (induced by source paper "toward learning a unified many to many mapping for diverse image translation"):
  with members:
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning depth from single images with deep neural network embedding focal length"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "diversified texture synthesis with feed forward networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial 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)