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 feature based contextual model for object detection"
  Abstract: Object detection is one of the most active areas in computer vision, which has made significant improvement in recent years. Current state-of-the-art object detection methods mostly adhere to the framework of regions with convolutional neural network (R-CNN) and only use local appearance features inside object bounding boxes. Since these approaches ignore the contextual information around the object proposals, the outcome of these detectors may generate a semantically incoherent interpretation of the input image. In this paper, we propose an ensemble object detection system which incorporates the local appearance, the contextual information in term of relationships among objects and the global scene based contextual feature generated by a convolutional neural network. The system is formulated as a fully connected conditional random field (CRF) defined on object proposals and the contextual constraints among object proposals are modeled as edges naturally. Furthermore, a fast mean field approximation method is utilized to inference in this CRF model efficiently. The experimental results demonstrate that our approach achieves a higher mean average precision (mAP) on PASCAL VOC 2007 datasets compared to the baseline algorithm Faster R-CNN.

Connected through hyperedge HE-1 (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-2 (induced by source paper "cross modal attentional context learning for rgb d object detection"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "dsod learning deeply supervised object detectors from scratch"
    - "instance level salient object segmentation"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "ssd single shot multibox detector"
    - "human pose estimation from depth images via inference embedded multi task learning"
    - "attentive contexts for object detection"
    - "visual saliency detection based on multiscale deep cnn features"

Connected through hyperedge HE-3 (induced by source paper "context aware zero shot learning for object recognition"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "modular generative adversarial networks"
    - "deep contextualized word representations"
    - "context encoding for semantic segmentation"
    - "semantic autoencoder for zero shot learning"
    - "learning transferable architectures for scalable image recognition"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-4 (induced by source paper "comparison based convolutional neural networks for cervical cell clumps detection in the limited data scenario"):
  with members:
    - "few example object detection with model communication"
    - "few shot object detection via feature reweighting"
    - "prototypical networks for few shot learning"
    - "single shot refinement neural network for object detection"
    - "learning to compare relation network for few shot learning"
    - "deeppap deep convolutional networks for cervical cell classification"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "cumulative link models for deep ordinal classification"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "automatic handgun detection alarm in videos using deep learning"
    - "deep ordinal ranking for multi category diagnosis of alzheimer s disease using hippocampal mri data"
    - "face detection using deep learning an improved faster rcnn approach"
    - "medical image retrieval using deep convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "on the importance of visual context for data augmentation in scene understanding"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "weakly supervised instance segmentation using class peak response"
    - "semantic foggy scene understanding with synthetic data"
    - "modeling visual context is key to augmenting object detection datasets"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "improved part segmentation performance by optimising realism of synthetic images using cycle generative adversarial networks"
    - "random erasing data augmentation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "modeling visual context is key to augmenting object detection datasets"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "random erasing data augmentation"
    - "focal loss for dense object detection"
    - "blitznet a real time deep network for scene understanding"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "dssd deconvolutional single shot detector"
    - "exploring the bounds of the utility of context for object detection"

Connected through hyperedge HE-8 (induced by source paper "right for the right reason training agnostic networks"):
  with members:
    - "addressing appearance change in outdoor robotics with adversarial domain adaptation"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "semantics derived automatically from language corpora contain human like biases"
    - "places an image database for deep scene understanding"
    - "why should i trust you explaining the predictions of any classifier"
    - "attentive contexts for object detection"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

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)