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: "exploiting web images for dataset construction a domain robust approach"
  Abstract: Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time-consuming and labor intensive. To reduce the cost of manual labeling, there has been increased research interest in automatically constructing image datasets by exploiting web images. Datasets constructed by existing methods tend to have a weak domain adaptation ability, which is known as the “dataset bias problem.” To address this issue, we present a novel image dataset construction framework that can be generalized well to unseen target domains. Specifically, the given queries are first expanded by searching the Google Books Ngrams Corpus to obtain a rich semantic description, from which the visually nonsalient and less relevant expansions are filtered out. By treating each selected expansion as a “bag” and the retrieved images as “instances,” image selection can be formulated as a multi-instance learning problem with constrained positive bags. We propose to solve the employed problems by the cutting-plane and concave-convex procedure algorithm. By using this approach, images from different distributions can be kept while noisy images are filtered out. To verify the effectiveness of our proposed approach, we build an image dataset with 20 categories. Extensive experiments on image classification, cross-dataset generalization, diversity comparison, and object detection demonstrate the domain robustness of our dataset.

Connected through hyperedge HE-1 (induced by source paper "c rpns promoting object detection in real world via a cascade structure of region proposal networks"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "cascade r cnn delving into high quality object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "harvesting visual objects from internet images via deep learning based objectness assessment"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "deepqoe a unified framework for learning to predict video qoe"
    - "instance level salient object segmentation"
    - "quasi homography warps in image stitching"
    - "instance sensitive fully convolutional networks"
    - "convolutional oriented boundaries"
    - "salient object subitizing"
    - "learning to refine object segments"

Connected through hyperedge HE-3 (induced by source paper "road segmentation with image lidar data fusion"):
  with members:
    - "dynamically visual disambiguation of keyword based image search"
    - "extracting visual knowledge from the internet making sense of image data"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "robust lstm autoencoders for face de occlusion in the wild"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "deep representation learning for road detection through siamese network"):
  with members:
    - "dynamically visual disambiguation of keyword based image search"
    - "extracting visual knowledge from the internet making sense of image data"
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "mask r cnn"
    - "multinet real time joint semantic reasoning for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "dynamically visual disambiguation of keyword based image search"):
  with members:
    - "deep representation learning for road detection through siamese network"
    - "hierarchical long short term concurrent memory for human interaction recognition"
    - "towards automatic construction of diverse high quality image dataset"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "frankenstein learning deep face representations using small data"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "extracting visual knowledge from the internet making sense of image data"):
  with members:
    - "towards automatic construction of diverse high quality image dataset"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "inductive hashing on manifolds"
    - "the google similarity distance"

Connected through hyperedge HE-7 (induced by source paper "learning from web data the benefit of unsupervised object localization"):
  with members:
    - "weakly supervised semantic segmentation using web crawled videos"
    - "cascade r cnn delving into high quality object detection"
    - "dsod learning deeply supervised object detectors from scratch"
    - "snn stacked neural networks"
    - "attend in groups a weakly supervised deep learning framework for learning from web data"
    - "yolo9000 better faster stronger"
    - "learning to segment object candidates"
    - "webly supervised learning of convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "exploiting web images for weakly supervised object detection"):
  with members:
    - "zero annotation object detection with web knowledge transfer"
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "multiple instance curriculum learning for weakly supervised object detection"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "multiple instance detection network with online instance classifier refinement"
    - "ssd single shot multibox detector"
    - "weakly supervised cascaded convolutional 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)