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: "first impressions a survey on vision based apparent personality trait analysis"
  Abstract: Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.

Connected through hyperedge HE-1 (induced by source paper "machines learn appearance bias in face recognition"):
  with members:
    - "saving face investigating the ethical concerns of facial recognition auditing"
    - "predictive inequity in object detection"
    - "explicit bias discovery in visual question answering models"
    - "women also snowboard overcoming bias in captioning models"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "semantics derived automatically from language corpora contain human like biases"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "robust regression via deep negative correlation learning"):
  with members:
    - "residual non local attention networks for image restoration"
    - "bag of tricks for image classification with convolutional neural networks"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "residual dense network for image super resolution"
    - "image super resolution via dual state recurrent networks"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"
    - "sphereface deep hypersphere embedding for face recognition"

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"
    - "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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "on the effect of observed subject biases in apparent personality analysis from audio visual signals"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "investigating audio visual and text fusion methods for end to end automatic personality prediction"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "age progression regression by conditional adversarial autoencoder"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features"
    - "deep impression audiovisual deep residual networks for multimodal apparent personality trait recognition"

Connected through hyperedge HE-5 (induced by source paper "background hardly matters understanding personality attribution in deep residual networks"):
  with members:
    - "explanation methods in deep learning users values concerns and challenges"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "a comprehensive survey of deep learning for image captioning"
    - "deep impression audiovisual deep residual networks for multimodal apparent personality trait recognition"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "persemon a deep network for joint analysis of apparent personality emotion and their relationship"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "sphereface deep hypersphere embedding for face recognition"
    - "facial affect estimation in the wild using deep residual and convolutional networks"
    - "emotion recognition in the wild using deep neural networks and bayesian classifiers"
    - "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features"
    - "deep impression audiovisual deep residual networks for multimodal apparent personality trait recognition"
    - "temporal segment networks towards good practices for deep action 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)