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 grimaces by watching tv"
  Abstract: Differently from computer vision systems which require explicit supervision, humans can learn facial expressions by observing people in their environment. In this paper, we look at how similar capabilities could be developed in machine vision. As a starting point, we consider the problem of relating facial expressions to objectively measurable events occurring in videos. In particular, we consider a gameshow in which contestants play to win significant sums of money. We extract events affecting the game and corresponding facial expressions objectively and automatically from the videos, obtaining large quantities of labelled data for our study. We also develop, using benchmarks such as FER and SFEW 2.0, state-of-the-art deep neural networks for facial expression recognition, showing that pre-training on face verification data can be highly beneficial for this task. Then, we extend these models to use facial expressions to predict events in videos and learn nameable expressions from them. The dataset and emotion recognition models are available at this http URL

Connected through hyperedge HE-1 (induced by source paper "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "facial affect estimation in the wild using deep residual and convolutional networks"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "emotion recognition in speech using cross modal transfer in the wild"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "deep learning is robust to massive label noise"
    - "self supervised learning of a facial attribute embedding from video"
    - "learnable pins cross modal embeddings for person identity"
    - "cross corpus speech emotion classification an effective transfer learning technique"
    - "exploring the limits of weakly supervised pretraining"
    - "on learning associations of faces and voices"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (induced by source paper "fine grained facial expression analysis using dimensional emotion model"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "low rank bilinear pooling for fine grained classification"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "multi column deep neural networks for image classification"

Connected through hyperedge HE-4 (induced by source paper "covariance pooling for facial expression recognition"):
  with members:
    - "second order convolutional neural networks"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "a riemannian network for spd matrix learning"

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)