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: "image as data automated visual content analysis for political science"
  Abstract: Image data provide unique information about political events, actors, and their interactions which are difficult to measure from or not available in text data. This article introduces a new class of automated methods based on computer vision and deep learning which can automatically analyze visual content data. Scholars have already recognized the importance of visual data and a variety of large visual datasets have become available. The lack of scalable analytic methods, however, has prevented from incorporating large scale image data in political analysis. This article aims to offer an in-depth overview of automated methods for visual content analysis and explains their usages and implementations. We further elaborate on how these methods and results can be validated and interpreted. We then discuss how these methods can contribute to the study of political communication, identity and politics, development, and conflict, by enabling a new set of research questions at scale.

Connected through hyperedge HE-1 (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"
    - "age progression regression by conditional adversarial autoencoder"
    - "how polarized have we become a multimodal classification of trump followers and clinton followers"
    - "protest activity detection and perceived violence estimation from social media images"
    - "from facial expression recognition to interpersonal relation prediction"
    - "deep residual learning for 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.CV(Computer Vision and Pattern Recognition)