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 pairwise ranking for multi label image classification"
  Abstract: Learning to rank has recently emerged as an attractive technique to train deep convolutional neural networks for various computer vision tasks. Pairwise ranking, in particular, has been successful in multi-label image classification, achieving state-of-the-art results on various benchmarks. However, most existing approaches use the hinge loss to train their models, which is non-smooth and thus is difficult to optimize especially with deep networks. Furthermore, they employ simple heuristics, such as top-k or thresholding, to determine which labels to include in the output from a ranked list of labels, which limits their use in the real-world setting. In this work, we propose two techniques to improve pairwise ranking based multi-label image classification: (1) we propose a novel loss function for pairwise ranking, which is smooth everywhere and thus is easier to optimize; and (2) we incorporate a label decision module into the model, estimating the optimal confidence thresholds for each visual concept. We provide theoretical analyses of our loss function in the Bayes consistency and risk minimization framework, and show its benefit over existing pairwise ranking formulations. We demonstrate the effectiveness of our approach on three large-scale datasets, VOC2007, NUS-WIDE and MS-COCO, achieving the best reported results in the literature.

Connected through hyperedge HE-1 (induced by source paper "top 1 solution of multi moments in time challenge 2019"):
  with members:
    - "a short note about kinetics 600"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "sgdr stochastic gradient descent with restarts"
    - "temporal segment networks towards good practices for deep action recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "dicenet dimension wise convolutions for efficient networks"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "discovering neural wirings"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "tvqa spatio temporal grounding for video question answering"):
  with members:
    - "neural baby talk"
    - "know what you don t know unanswerable questions for squad"
    - "mattnet modular attention network for referring expression comprehension"
    - "grounded video description"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tvqa localized compositional video question answering"
    - "bsn boundary sensitive network for temporal action proposal generation"

Connected through hyperedge HE-4 (induced by source paper "exploring uncertainty in conditional multi modal retrieval systems"):
  with members:
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "features for multi target multi camera tracking and re identification"
    - "two stream self supervised learning for action recognition"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "efficient uncertainty estimation for semantic segmentation in videos"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"

Connected through hyperedge HE-5 (induced by source paper "exploring uncertainty measures for image caption embedding and retrieval task"):
  with members:
    - "a closer look at few shot classification"
    - "exploring uncertainty in conditional multi modal retrieval systems"
    - "quantifying uncertainties in natural language processing tasks"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "object hallucination in image captioning"
    - "anomaly machine component detection by deep generative model with unregularized score"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"

Connected through hyperedge HE-6 (induced by source paper "target aware deep tracking"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "context aware deep feature compression for high speed visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "deep attentive tracking via reciprocative learning"
    - "unveiling the power of deep tracking"
    - "correlation tracking via joint discrimination and reliability learning"

Connected through hyperedge HE-7 (induced by source paper "elucidating image to set prediction an analysis of models losses and datasets"):
  with members:
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "multi label learning with missing labels using mixed dependency graphs"
    - "exploring the limits of weakly supervised pretraining"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "loss functions for multiset prediction"
    - "attention is all you need"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning deep latent spaces for multi label classification"

Connected through hyperedge HE-8 (induced by source paper "orderless recurrent models for multi label classification"):
  with members:
    - "elucidating image to set prediction an analysis of models losses and datasets"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "a comprehensive survey of deep learning for image captioning"
    - "averaging weights leads to wider optima and better generalization"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "learning spatial regularization with image level supervisions for multi label image classification"

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)