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: "learning social relation traits from face images"
  Abstract: Social 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 characterised and quantified from face images in the wild. To address this challenging problem we propose a deep model that learns a rich face representation to capture gender, expression, head pose, and age-related attributes, and then performs pairwise-face reasoning for relation prediction. To learn from heterogeneous attribute sources, we formulate a new network architecture with a bridging layer to leverage the inherent correspondences among these datasets. It can also cope with missing target attribute labels. Extensive experiments show that our approach is effective for fine-grained social relation learning in images and videos.

Connected through hyperedge HE-1 (induced by source paper "image captioning using facial expression and attention"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "face cap image captioning using facial expression analysis"
    - "automatic recognition of student engagement using deep learning and facial expression"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "a comprehensive survey of deep learning for image captioning"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"

Connected through hyperedge HE-2 (induced by source paper "fairface face attribute dataset for balanced race gender and age"):
  with members:
    - "diversity in faces"
    - "understanding the political ideology of legislators from social media images"
    - "persuasive faces generating faces in advertisements"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "mitigating unwanted biases with adversarial learning"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (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"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-4 (induced by source paper "learning fashion compatibility across apparel categories for outfit recommendation"):
  with members:
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "learning type aware embeddings for fashion compatibility"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "creating capsule wardrobes from fashion images"
    - "exploiting restricted boltzmann machines and deep belief networks in compressed sensing"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "learning compatibility across categories for heterogeneous item recommendation"

Connected through hyperedge HE-5 (induced by source paper "an end to end network for generating social relationship graphs"):
  with members:
    - "person recognition in personal photo collections"
    - "a domain based approach to social relation recognition"
    - "neural motifs scene graph parsing with global context"
    - "dual glance model for deciphering social relationships"
    - "scene graph generation by iterative message passing"
    - "scene graph generation from objects phrases and region captions"
    - "gated graph sequence neural networks"
    - "spice semantic propositional image caption evaluation"

Connected through hyperedge HE-6 (induced by source paper "graph representation for face analysis in image collections"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "arcface additive angular margin loss for deep face recognition"
    - "a style based generator architecture for generative adversarial networks"
    - "face clustering representation and pairwise constraints"
    - "real time convolutional neural networks for emotion and gender classification"
    - "fine grained head pose estimation without keypoints"
    - "face detection using deep learning an improved faster rcnn approach"
    - "clustering millions of faces by identity"

Connected through hyperedge HE-7 (induced by source paper "deep facial expression recognition a survey"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep covariance descriptors for facial expression recognition"
    - "geometry contrastive gan for facial expression transfer"
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "covariance pooling for facial expression recognition"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "accurate facial parts localization and deep learning for 3d facial expression recognition"
    - "island loss for learning discriminative features in facial expression recognition"

Connected through hyperedge HE-8 (induced by source paper "visual social relationship recognition"):
  with members:
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "deep label distribution learning with label ambiguity"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "focal loss for dense object detection"
    - "a domain based approach to social relation recognition"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "moviegraphs towards understanding human centric situations from videos"
    - "dual glance model for deciphering social relationships"

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)