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: "viraliency pooling local virality"
  Abstract: In our overly-connected world, the automatic recognition of virality - the quality of an image or video to be rapidly and widely spread in social networks - is of crucial importance, and has recently awaken the interest of the computer vision community. Concurrently, recent progress in deep learning architectures showed that global pooling strategies allow the extraction of activation maps, which highlight the parts of the image most likely to contain instances of a certain class. We extend this concept by introducing a pooling layer that learns the size of the support area to be averaged: the learned top-N average (LENA) pooling. We hypothesize that the latent concepts (feature maps) describing virality may require such a rich pooling strategy. We assess the effectiveness of the LENA layer by appending it on top of a convolutional siamese architecture and evaluate its performance on the task of predicting and localizing virality. We report experiments on two publicly available datasets annotated for virality and show that our method outperforms state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "integrating text and image determining multimodal document intent in instagram posts"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "deep contextualized word representations"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "understanding visual ads by aligning symbols and objects using co attention"
    - "knowledge rich image gist understanding beyond literal meaning"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive analysis of deep regression"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "lstm a search space odyssey"
    - "learning feature pyramids for human pose estimation"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "deep convolutional neural network design patterns"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "on architectural choices in deep learning from network structure to gradient convergence and parameter estimation"

Connected through hyperedge HE-3 (induced by source paper "cube padding for weakly supervised saliency prediction in 360 deg videos"):
  with members:
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "end to end saliency mapping via probability distribution prediction"
    - "saltinet scan path prediction on 360 degree images using saliency volumes"
    - "the 2017 davis challenge on video object segmentation"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "semantic driven generation of hyperlapse from 360 circ video"
    - "learning video object segmentation with visual memory"
    - "salnet360 saliency maps for omni directional images with cnn"

Connected through hyperedge HE-4 (induced by source paper "deepgum learning deep robust regression with a gaussian uniform mixture model"):
  with members:
    - "a comprehensive analysis of deep regression"
    - "how to make an image more memorable a deep style transfer approach"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "learning from noisy labels with distillation"
    - "em algorithms for weighted data clustering with application to audio visual scene analysis"
    - "fashion landmark detection in the wild"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "robust optimization for deep regression"

Connected through hyperedge HE-5 (induced by source paper "pay attention to virality understanding popularity of social media videos with the attention mechanism"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "recurrent neural networks for online video popularity prediction"
    - "shallow reading with deep learning predicting popularity of online content using only its title"
    - "predicting popularity of online videos using support vector regression"
    - "understanding image virality"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "how to make an image more memorable a deep style transfer approach"):
  with members:
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "a neural algorithm of artistic style"

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)