human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "emotion recognition system from speech and visual information based on convolutional neural networks"):
  Abstract: Emotion recognition has become an important field of research in the human-computer interactions domain. The latest advancements in the field show that combining visual with audio information lead to better results if compared to the case of using a single source of information separately. From a visual point of view, a human emotion can be recognized by analyzing the facial expression of the person. More precisely, the human emotion can be described through a combination of several Facial Action Units. In this paper, we propose a system that is able to recognize emotions with a high accuracy rate and in real time, based on deep Convolutional Neural Networks. In order to increase the accuracy of the recognition system, we analyze also the speech data and fuse the information coming from both sources, i.e., visual and audio. Experimental results show the effectiveness of the proposed scheme for emotion recognition and the importance of combining visual with audio data.

Includes members:
  Member "realistic speech driven facial animation with gans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "the omg emotion behavior dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multitask emotion recognition with incomplete labels")
  Member "deep facial expression recognition a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses")
  Member "end to end multimodal emotion recognition using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi task learning with auxiliary speaker identification for conversational emotion recognition")
  Member "dexpression deep convolutional neural network for expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recognition of advertisement emotions with application to computational advertising")
  Member "emonets multimodal deep learning approaches for emotion recognition in video":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stroke constrained attention network for online handwritten mathematical expression recognition")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)