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: "object scene convolutional neural networks for event recognition in images"
  Abstract: Event recognition from still images is of great importance for image understanding. However, compared with event recognition in videos, there are much fewer research works on event recognition in images. This paper addresses the issue of event recognition from images and proposes an effective method with deep neural networks. Specifically, we design a new architecture, called Object-Scene Convolutional Neural Network (OS-CNN). This architecture is decomposed into object net and scene net, which extract useful information for event understanding from the perspective of objects and scene context, respectively. Meanwhile, we investigate different network architectures for OS-CNN design, and adapt the deep (AlexNet) and very-deep (GoogLeNet) networks to the task of event recognition. Furthermore, we find that the deep and very-deep networks are complementary to each other. Finally, based on the proposed OS-CNN and comparative study of different network architectures, we come up with a solution of five-stream CNN for the track of cultural event recognition at the ChaLearn Looking at People (LAP) challenge 2015. Our method obtains the performance of 85.5% and ranks the $1^{st}$ place in this challenge.

Connected through hyperedge HE-1 (induced by source paper "graph neural networks for image understanding based on multiple cues group emotion recognition and event recognition as use cases"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "smile detection in the wild based on transfer learning"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "complex event recognition from images with few training examples"
    - "neural message passing for quantum chemistry"
    - "squeeze and excitation networks"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-2 (induced by source paper "hierarchical approach to classify food scenes in egocentric photo streams"):
  with members:
    - "batch based activity recognition from egocentric photo streams revisited"
    - "macnet multi scale atrous convolution networks for food places classification in egocentric photo streams"
    - "r clustering for egocentric video segmentation"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "places an image database for deep scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "scene recognition with cnns objects scales and dataset bias"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-3 (induced by source paper "dynamic deep multi modal fusion for image privacy prediction"):
  with members:
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "places an image database for deep scene understanding"
    - "convolutional two stream network fusion for video action recognition"
    - "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce"
    - "images don t lie transferring deep visual semantic features to large scale multimodal learning to rank"
    - "meta des"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-4 (induced by source paper "s dod cnn doubly injecting spatially preserved object information for event recognition"):
  with members:
    - "dod cnn doubly injecting object information for event recognition"
    - "iod cnn integrating object detection networks for event recognition"
    - "enhanced object detection via fusion with prior beliefs from image classification"
    - "exploitation of semantic keywords for malicious event classification"
    - "dynamic belief fusion for object detection"
    - "better exploiting os cnns for better event recognition in images"
    - "detection bank an object detection based video representation for multimedia event recognition"

Connected through hyperedge HE-5 (induced by source paper "dod cnn doubly injecting object information for event recognition"):
  with members:
    - "learning to detect human object interactions"
    - "iod cnn integrating object detection networks for event recognition"
    - "enhanced object detection via fusion with prior beliefs from image classification"
    - "exploitation of semantic keywords for malicious event classification"
    - "dynamic belief fusion for object detection"
    - "detection bank an object detection based video representation for multimedia event recognition"

Connected through hyperedge HE-6 (induced by source paper "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "locally supervised deep hybrid model for scene recognition"
    - "transferring object scene convolutional neural networks for event recognition in still images"
    - "real time action recognition with enhanced motion vector cnns"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "places an image database for deep scene understanding"
    - "weakly supervised fine grained categorization with part based image representation"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised patchnets describing and aggregating local patches for scene recognition"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "locally supervised deep hybrid model for scene recognition"
    - "transferring object scene convolutional neural networks for event recognition in still images"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "actionness estimation using hybrid fully convolutional networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "local color contrastive descriptor for image classification"

Connected through hyperedge HE-8 (induced by source paper "semantic image retrieval via active grounding of visual situations"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "grounding of textual phrases in images by reconstruction"
    - "modeling context between objects for referring expression understanding"
    - "visual relationship detection with language priors"
    - "generation and comprehension of unambiguous object descriptions"
    - "show attend and tell neural image caption generation with visual attention"
    - "visual semantic role labeling"
    - "active object localization with deep reinforcement learning"

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)