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: "improving image classification with location context"
  Abstract: With the widespread availability of cellphones and cameras that have GPS capabilities, it is common for images being uploaded to the Internet today to have GPS coordinates associated with them. In addition to research that tries to predict GPS coordinates from visual features, this also opens up the door to problems that are conditioned on the availability of GPS coordinates. In this work, we tackle the problem of performing image classification with location context, in which we are given the GPS coordinates for images in both the train and test phases. We explore different ways of encoding and extracting features from the GPS coordinates, and show how to naturally incorporate these features into a Convolutional Neural Network (CNN), the current state-of-the-art for most image classification and recognition problems. We also show how it is possible to simultaneously learn the optimal pooling radii for a subset of our features within the CNN framework. To evaluate our model and to help promote research in this area, we identify a set of location-sensitive concepts and annotate a subset of the Yahoo Flickr Creative Commons 100M dataset that has GPS coordinates with these concepts, which we make publicly available. By leveraging location context, we are able to achieve almost a 7% gain in mean average precision.

Connected through hyperedge HE-1 (induced by source paper "don t judge an object by its context learning to overcome contextual bias"):
  with members:
    - "does object recognition work for everyone"
    - "mitigating gender bias in natural language processing literature review"
    - "repair removing representation bias by dataset resampling"
    - "class balanced loss based on effective number of samples"
    - "laso label set operations networks for multi label few shot learning"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "learning not to learn training deep neural networks with biased data"

Connected through hyperedge HE-2 (induced by source paper "multi scale representation learning for spatial feature distributions using grid cells"):
  with members:
    - "geo aware networks for fine grained recognition"
    - "contextual graph attention for answering logical queries over incomplete knowledge graphs"
    - "presence only geographical priors for fine grained image classification"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "context aware image annotation in active learning"):
  with members:
    - "how does tweet difficulty affect labeling performance of annotators"
    - "deep convolutional ranking for multilabel image annotation"
    - "image labeling on a network using social network metadata for image classification"

Connected through hyperedge HE-4 (induced by source paper "geo aware networks for fine grained recognition"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the devil is in the tails fine grained classification in the wild"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "cvae gan fine grained image generation through asymmetric training"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-5 (induced by source paper "decision propagation networks for image classification"):
  with members:
    - "cinic 10 is not imagenet or cifar 10"
    - "ensemble soft margin softmax loss for image classification"
    - "class2str end to end latent hierarchy learning"
    - "do convolutional neural networks learn class hierarchy"
    - "deep forest towards an alternative to deep neural networks"
    - "deep convolutional decision jungle for image classification"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "improving pairwise ranking for multi label image classification"

Connected through hyperedge HE-6 (induced by source paper "presence only geographical priors for fine grained image classification"):
  with members:
    - "geo aware networks for fine grained recognition"
    - "class balanced loss based on effective number of samples"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "bias reduction via end to end shift learning application to citizen science"
    - "multi entity dependence learning with rich context via conditional variational auto encoder"
    - "pairwise confusion for fine grained visual classification"
    - "diversified visual attention networks for fine grained object classification"
    - "residual attention network for image classification"

Connected through hyperedge HE-7 (induced by source paper "a cnn rnn framework for image annotation from visual cues and social network metadata"):
  with members:
    - "modeling visual context is key to augmenting object detection datasets"
    - "deep neural networks in fully connected crf for image labeling with social network metadata"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "automatic image annotation via label transfer in the semantic space"
    - "semantic regularisation for recurrent image annotation"
    - "cnn rnn a unified framework for multi label image classification"
    - "webly supervised learning of convolutional networks"
    - "learning structured inference neural networks with label relations"

Connected through hyperedge HE-8 (induced by source paper "mobile recognition of wikipedia featured sites using deep learning and crowd sourced imagery"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the inaturalist species classification and detection dataset"
    - "using deep learning for image based plant disease detection"
    - "planet photo geolocation with convolutional neural networks"
    - "modelling uncertainty in deep learning for camera relocalization"

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)