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: "deep impression audiovisual deep residual networks for multimodal apparent personality trait recognition"
  Abstract: Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "seeing and hearing egocentric actions how much can we learn"):
  with members:
    - "fbk hupba submission to the epic kitchens 2019 action recognition challenge"
    - "uncertainty aware audiovisual activity recognition using deep bayesian variational inference"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "audio based activities of daily living adl recognition with large scale acoustic embeddings from online videos"
    - "how much does audio matter to recognize egocentric object interactions"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "lsta long short term attention for egocentric action recognition"

Connected through hyperedge HE-3 (induced by source paper "multimodal video based apparent personality recognition using long short term memory and convolutional neural networks"):
  with members:
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features"
    - "identity mappings in deep residual networks"
    - "cnn architectures for large scale audio classification"
    - "modern physiognomy an investigation on predicting personality traits and intelligence from the human face"
    - "youtube 8m a large scale video classification benchmark"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

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"
    - "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"

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"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "a comprehensive survey of deep learning for image captioning"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "towards universal end to end affect recognition from multilingual speech by convnets"):
  with members:
    - "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features"
    - "towards empathetic human robot interactions"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (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"
    - "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"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-8 (induced by source paper "first impressions a survey on vision based apparent personality trait analysis"):
  with members:
    - "survey on emotional body gesture recognition"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "semantics derived automatically from language corpora contain human like biases"
    - "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features"
    - "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)