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: "explaining first impressions modeling recognizing and explaining apparent personality from videos"
  Abstract: Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.

Connected through hyperedge HE-1 (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"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "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-2 (induced by source paper "recent trends in deep learning based personality detection"):
  with members:
    - "sentiment and sarcasm classification with multitask learning"
    - "investigating audio visual and text fusion methods for end to end automatic personality prediction"
    - "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "deep residual learning for image recognition"
    - "pr2 a language independent unsupervised tool for personality recognition from text"

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"
    - "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"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "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"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "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"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "first impressions a survey on vision based apparent personality trait analysis"):
  with members:
    - "survey on emotional body gesture recognition"
    - "semantics derived automatically from language corpora contain human like biases"
    - "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"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "action recognition with dynamic image networks"

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)