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: "face cap image captioning using facial expression analysis"
  Abstract: Image captioning is the process of generating a natural language description of an image. Most current image captioning models, however, do not take into account the emotional aspect of an image, which is very relevant to activities and interpersonal relationships represented therein. Towards developing a model that can produce human-like captions incorporating these, we use facial expression features extracted from images including human faces, with the aim of improving the descriptive ability of the model. In this work, we present two variants of our Face-Cap model, which embed facial expression features in different ways, to generate image captions. Using all standard evaluation metrics, our Face-Cap models outperform a state-of-the-art baseline model for generating image captions when applied to an image caption dataset extracted from the standard Flickr 30K dataset, consisting of around 11K images containing faces. An analysis of the captions finds that, perhaps surprisingly, the improvement in caption quality appears to come not from the addition of adjectives linked to emotional aspects of the images, but from more variety in the actions described in the captions.

Connected through hyperedge HE-1 (induced by source paper "safenet an assistive solution to assess incoming threats for premises"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deepid3 face recognition with very deep neural networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "image captioning using facial expression and attention"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "text2facegan face generation from fine grained textual descriptions"):
  with members:
    - "wav2pix speech conditioned face generation using generative adversarial networks"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "category based deep cca for fine grained venue discovery from multimodal data"
    - "face2text collecting an annotated image description corpus for the generation of rich face descriptions"
    - "a style based generator architecture for generative adversarial networks"
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-4 (induced by source paper "person identification with visual summary for a safe access to a smart home"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mask r cnn"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deepid3 face recognition with very deep neural networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "senti attend image captioning using sentiment and attention"):
  with members:
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "affect lm a neural language model for customizable affective text generation"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"

Connected through hyperedge HE-6 (induced by source paper "automatic recognition of student engagement using deep learning and facial expression"):
  with members:
    - "facial expression recognition using convolutional neural networks state of the art"
    - "going deeper in facial expression recognition using deep neural networks"
    - "daisee towards user engagement recognition in the wild"
    - "learning social relation traits from face images"
    - "emonets multimodal deep learning approaches for emotion recognition in video"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning using linear support vector machines"

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)