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 "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"):
  Abstract: Automatic understanding of human affect using visual signals is of great importance in everyday human–machine interactions. Appraising human emotional states, behaviors and reactions displayed in real-world settings, can be accomplished using latent continuous dimensions (e.g., the circumplex model of affect). Valence (i.e., how positive or negative is an emotion) and arousal (i.e., power of the activation of the emotion) constitute popular and effective representations for affect. Nevertheless, the majority of collected datasets this far, although containing naturalistic emotional states, have been captured in highly controlled recording conditions. In this paper, we introduce the Aff-Wild benchmark for training and evaluating affect recognition algorithms. We also report on the results of the First Affect-in-the-wild Challenge (Aff-Wild Challenge) that was recently organized in conjunction with CVPR 2017 on the Aff-Wild database, and was the first ever challenge on the estimation of valence and arousal in-the-wild. Furthermore, we design and extensively train an end-to-end deep neural architecture which performs prediction of continuous emotion dimensions based on visual cues. The proposed deep learning architecture, AffWildNet, includes convolutional and recurrent neural network layers, exploiting the invariant properties of convolutional features, while also modeling temporal dynamics that arise in human behavior via the recurrent layers. The AffWildNet produced state-of-the-art results on the Aff-Wild Challenge. We then exploit the AffWild database for learning features, which can be used as priors for achieving best performances both for dimensional, as well as categorical emotion recognition, using the RECOLA, AFEW-VA and EmotiW 2017 datasets, compared to all other methods designed for the same goal. The database and emotion recognition models are available at #N#                  http://ibug.doc.ic.ac.uk/resources/first-affect-wild-challenge#N#                  #N#                .

Includes members:
  Member "a comprehensive performance evaluation of deformable face tracking in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "facial landmark detection a literature survey")
  Member "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "frame attention networks for facial expression recognition in videos")
  Member "temporal multimodal fusion for video emotion classification in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "factorized multimodal transformer for multimodal sequential learning")
  Member "facial affect estimation in the wild using deep residual and convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bounded residual gradient networks breg net for facial affect computing")
  Member "avec 2016 depression mood and emotion recognition workshop and challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for human affect recognition insights and new developments")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "learning grimaces by watching tv":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "emotion recognition in speech using cross modal transfer in the wild")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)