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: "from facial expression recognition to interpersonal relation prediction"
  Abstract: Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. Motivated by psychological studies, we investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.

Connected through hyperedge HE-1 (induced by source paper "ae ot gan training gans from data specific latent distribution"):
  with members:
    - "maskgan towards diverse and interactive facial image manipulation"
    - "diagnosing and enhancing vae models"
    - "large scale adversarial representation learning"
    - "generating diverse high fidelity images with vq vae 2"
    - "disconnected manifold learning for generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "expression analysis based on face regions in read world conditions"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "attention is all you need"
    - "affect lm a neural language model for customizable affective text generation"
    - "dual path networks"
    - "affective neural response generation"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "densely connected convolutional networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-3 (induced by source paper "beyond without forgetting multi task learning for classification with disjoint datasets"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "semisupervised feature analysis by mining correlations among multiple tasks"
    - "learning from noisy labels with distillation"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"

Connected through hyperedge HE-4 (induced by source paper "multitask emotion recognition with incomplete labels"):
  with members:
    - "analysing affective behavior in the first abaw 2020 competition"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "face behavior a la carte expressions affect and action units in a single network"
    - "mimamo net integrating micro and macro motion for video emotion recognition"
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "the omg emotion behavior dataset"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "emotionet challenge recognition of facial expressions of emotion in the wild"

Connected through hyperedge HE-5 (induced by source paper "fau facial expressions valence and arousal a multi task solution"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "aff wild2 extending the aff wild database for affect recognition"

Connected through hyperedge HE-6 (induced by source paper "multi granularity reasoning for social relation recognition from images"):
  with members:
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "simple baselines for human pose estimation and tracking"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "a domain based approach to social relation recognition"
    - "dual glance model for deciphering social relationships"
    - "mask r cnn"
    - "semantic object parsing with graph lstm"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-7 (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"
    - "protest activity detection and perceived violence estimation from social media images"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "feature level and model level audiovisual fusion for emotion recognition in the wild"):
  with members:
    - "identity free facial expression recognition using conditional generative adversarial network"
    - "deep texture manifold for ground terrain recognition"
    - "probabilistic attribute tree in convolutional neural networks for facial expression recognition"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "island loss for learning discriminative features in facial expression recognition"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "optimizing filter size in convolutional neural networks for facial action unit 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)