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: "dexpression deep convolutional neural network for expression recognition"
  Abstract: We propose a convolutional neural network (CNN) architecture for facial expression recognition. The proposed architecture is independent of any hand-crafted feature extraction and performs better than the earlier proposed convolutional neural network based approaches. We visualize the automatically extracted features which have been learned by the network in order to provide a better understanding. The standard datasets, i.e. Extended Cohn-Kanade (CKP) and MMI Facial Expression Databse are used for the quantitative evaluation. On the CKP set the current state of the art approach, using CNNs, achieves an accuracy of 99.2%. For the MMI dataset, currently the best accuracy for emotion recognition is 93.33%. The proposed architecture achieves 99.6% for CKP and 98.63% for MMI, therefore performing better than the state of the art using CNNs. Automatic facial expression recognition has a broad spectrum of applications such as human-computer interaction and safety systems. This is due to the fact that non-verbal cues are important forms of communication and play a pivotal role in interpersonal communication. The performance of the proposed architecture endorses the efficacy and reliable usage of the proposed work for real world applications.

Connected through hyperedge HE-1 (induced by source paper "recognition of advertisement emotions with application to computational advertising"):
  with members:
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "affect recognition in ads with application to computational advertising"
    - "learning transferable architectures for scalable image recognition"
    - "deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders"
    - "evaluating content centric vs user centric ad affect recognition"
    - "discovering gender differences in facial emotion recognition via implicit behavioral cues"
    - "object detection can be improved using human derived contextual expectations"
    - "deep feature learning for eeg recordings"

Connected through hyperedge HE-2 (induced by source paper "expertnet exigent features preservative network for facial expression recognition"):
  with members:
    - "spatio temporal facial expression recognition using convolutional neural networks and conditional random fields"
    - "deep generative contrastive networks for facial expression recognition"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "deep residual learning for image recognition"
    - "deep temporal appearance geometry network for facial expression recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "emotion recognition system from speech and visual information based on convolutional neural networks"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "the omg emotion behavior dataset"
    - "deep facial expression recognition a survey"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "emonets multimodal deep learning approaches for emotion recognition in video"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "it all matters reporting accuracy inference time and power consumption for face emotion recognition on embedded systems"):
  with members:
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "sention a framework for sensing facial expressions"):
  with members:
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "automatic facial expression recognition using features of salient facial patches"
    - "geometric feature based facial expression recognition in image sequences using multi class adaboost and support vector machines"
    - "a real time facial expression classification system using local binary patterns"

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)