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: "the role of context selection in object detection"
  Abstract: We investigate the reasons why context in object detection has limited utility by isolating and evaluating the predictive power of different context cues under ideal conditions in which context provided by an oracle. Based on this study, we propose a region-based context re-scoring method with dynamic context selection to remove noise and emphasize informative context. We introduce latent indicator variables to select (or ignore) potential contextual regions, and learn the selection strategy with latent-SVM. We conduct experiments to evaluate the performance of the proposed context selection method on the SUN RGB-D dataset. The method achieves a significant improvement in terms of mean average precision (mAP), compared with both appearance based detectors and a conventional context model without the selection scheme.

Connected through hyperedge HE-1 (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-2 (induced by source paper "seeing without looking contextual rescoring of object detections for ap maximization"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "attention is all you need"
    - "inner scene similarities as a contextual cue for object detection"
    - "cascade r cnn delving into high quality object detection"
    - "learning non maximum suppression"
    - "spatial memory for context reasoning in object detection"
    - "exploring the bounds of the utility of context for object detection"

Connected through hyperedge HE-3 (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-4 (induced by source paper "modeling local geometric structure of 3d point clouds using geo cnn"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "multiresolution tree networks for 3d point cloud processing"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "local spectral graph convolution for point set feature learning"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "fully convolutional point networks for large scale point clouds"

Connected through hyperedge HE-5 (induced by source paper "zero shot object detection"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "recent advances in zero shot recognition"
    - "visually aligned word embeddings for improving zero shot learning"

Connected through hyperedge HE-6 (induced by source paper "remotenet efficient relevant motion event detection for large scale home surveillance videos"):
  with members:
    - "viton an image based virtual try on network"
    - "nisp pruning networks using neuron importance score propagation"
    - "c wsl count guided weakly supervised localization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "dynamic zoom in network for fast object detection in large images"
    - "an overview of multi task learning in deep neural networks"
    - "generating holistic 3d scene abstractions for text based image retrieval"

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 "learning rich features for image manipulation detection"):
  with members:
    - "image splicing localization using a multi task fully convolutional network mfcn"
    - "two stream neural networks for tampered face detection"
    - "recasting residual based local descriptors as convolutional neural networks an application to image forgery detection"
    - "deep regionlets for object detection"
    - "dynamic zoom in network for fast object detection in large images"
    - "r fcn object detection via region based fully convolutional networks"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual 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)