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: "protest activity detection and perceived violence estimation from social media images"
  Abstract: We develop a novel visual model which can recognize protesters, describe their activities by visual attributes and estimate the level of perceived violence in an image. Studies of social media and protests use natural language processing to track how individuals use hashtags and links, often with a focus on those items' diffusion. These approaches, however, may not be effective in fully characterizing actual real-world protests (e.g., violent or peaceful) or estimating the demographics of participants (e.g., age, gender, and race) and their emotions. Our system characterizes protests along these dimensions. We have collected geotagged tweets and their images from 2013-2017 and analyzed multiple major protest events in that period. A multi-task convolutional neural network is employed in order to automatically classify the presence of protesters in an image and predict its visual attributes, perceived violence and exhibited emotions. We also release the UCLA Protest Image Dataset, our novel dataset of 40,764 images (11,659 protest images and hard negatives) with various annotations of visual attributes and sentiments. Using this dataset, we train our model and demonstrate its effectiveness. We also present experimental results from various analysis on geotagged image data in several prevalent protest events. Our dataset will be made accessible at this https URL

Connected through hyperedge HE-1 (induced by source paper "fairface face attribute dataset for balanced race gender and age"):
  with members:
    - "diversity in faces"
    - "understanding the political ideology of legislators from social media images"
    - "persuasive faces generating faces in advertisements"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "mitigating unwanted biases with adversarial learning"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "deep neural network for semantic based text recognition in images"):
  with members:
    - "global encoding for abstractive summarization"
    - "attention based encoder decoder networks for spelling and grammatical error correction"
    - "edit probability for scene text recognition"
    - "opennmt open source toolkit for neural machine translation"
    - "neural networks for text correction and completion in keyboard decoding"
    - "cold fusion training seq2seq models together with language models"
    - "text attentional convolutional neural network for scene text detection"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-3 (induced by source paper "understanding the political ideology of legislators from social media images"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "image as data automated visual content analysis for political science"
    - "age progression regression by conditional adversarial autoencoder"
    - "how polarized have we become a multimodal classification of trump followers and clinton followers"
    - "from facial expression recognition to interpersonal relation prediction"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "story understanding in video advertisements"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a read write memory network for movie story understanding"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "advise symbolism and external knowledge for decoding advertisements"
    - "stacked hourglass networks for human pose estimation"
    - "optical flow estimation using a spatial pyramid network"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-5 (induced by source paper "image as data automated visual content analysis for political science"):
  with members:
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "generating visual explanations"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"
    - "vqa visual question answering"
    - "probabilistic latent semantic analysis"

Connected through hyperedge HE-6 (induced by source paper "user driven mobile robot storyboarding learning image interest and saliency from pairwise image comparisons"):
  with members:
    - "toward storytelling from visual lifelogging an overview"
    - "deep learning the city quantifying urban perception at a global scale"
    - "deep residual learning for image recognition"
    - "visual summary of egocentric photostreams by representative keyframes"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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.MM(Multimedia)