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: "squared earth mover s distance based loss for training deep neural networks"
  Abstract: In the context of single-label classification, despite the huge success of deep learning, the commonly used cross-entropy loss function ignores the intricate inter-class relationships that often exist in real-life tasks such as age classification. In this work, we propose to leverage these relationships between classes by training deep nets with the exact squared Earth Mover's Distance (also known as Wasserstein distance) for single-label classification. The squared EMD loss uses the predicted probabilities of all classes and penalizes the miss-predictions according to a ground distance matrix that quantifies the dissimilarities between classes. We demonstrate that on datasets with strong inter-class relationships such as an ordering between classes, our exact squared EMD losses yield new state-of-the-art results. Furthermore, we propose a method to automatically learn this matrix using the CNN's own features during training. We show that our method can learn a ground distance matrix efficiently with no inter-class relationship priors and yield the same performance gain. Finally, we show that our method can be generalized to applications that lack strong inter-class relationships and still maintain state-of-the-art performance. Therefore, with limited computational overhead, one can always deploy the proposed loss function on any dataset over the conventional cross-entropy.

Connected through hyperedge HE-1 (induced by source paper "deep ordinal regression for pledge specificity prediction"):
  with members:
    - "target based speech act classification in political campaign text"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "deep contextualized word representations"
    - "hierarchical structured model for fine to coarse manifesto text analysis"
    - "semi supervised sequence modeling with cross view training"
    - "predicting specificity in classroom discussion"
    - "deep label distribution learning with label ambiguity"

Connected through hyperedge HE-2 (induced by source paper "hue net intensity based image to image translation with differentiable histogram loss functions"):
  with members:
    - "neural color transfer between images"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "ordinal distribution regression for gait based age estimation"):
  with members:
    - "bridgenet a continuity aware probabilistic network for age estimation"
    - "label distribution learning forests"
    - "deep regression forests for age estimation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "deep learning based image super resolution considering quantitative and perceptual quality"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "maintaining natural image statistics with the contextual loss"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "the perception distortion tradeoff"
    - "nima neural image assessment"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-5 (induced by source paper "fine grained age estimation in the wild with attention lstm networks"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "deep label distribution learning with label ambiguity"
    - "age group and gender estimation in the wild with deep ror architecture"
    - "video summarization with attention based encoder decoder networks"
    - "dager deep age gender and emotion recognition using convolutional neural network"
    - "pyramidal ror for image classification"
    - "apparent age estimation using ensemble of deep learning models"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-6 (induced by source paper "nima neural image assessment"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "on the use of deep learning for blind image quality assessment"
    - "learned perceptual image enhancement"
    - "a probabilistic quality representation approach to deep blind image quality prediction"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a color intensity invariant low level feature optimization framework for image quality assessment"
    - "visual aesthetic quality assessment with multi task deep learning"
    - "brain inspired deep networks for image aesthetics assessment"

Connected through hyperedge HE-7 (induced by source paper "learning to rank for censored survival data"):
  with members:
    - "deep learning for patient specific kidney graft survival analysis"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning with a wasserstein loss"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-8 (induced by source paper "learned perceptual image enhancement"):
  with members:
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "style transfer via texture synthesis"
    - "fast image processing with fully convolutional networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "visual aesthetic quality assessment with multi task deep 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)