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 "inferring dynamic representations of facial actions from a still image"):
  Abstract: Facial actions are spatio-temporal signals by nature, and therefore their modeling is crucially dependent on the availability of temporal information. In this paper, we focus on inferring such temporal dynamics of facial actions when no explicit temporal information is available, i.e. from still images. We present a novel approach to capture multiple scales of such temporal dynamics, with an application to facial Action Unit (AU) intensity estimation and dimensional affect estimation. In particular, 1) we propose a framework that infers a dynamic representation (DR) from a still image, which captures the bi-directional flow of time within a short time-window centered at the input image; 2) we show that we can train our method without the need of explicitly generating target representations, allowing the network to represent dynamics more broadly; and 3) we propose to apply a multiple temporal scale approach that infers DRs for different window lengths (MDR) from a still image. We empirically validate the value of our approach on the task of frame ranking, and show how our proposed MDR attains state of the art results on BP4D for AU intensity estimation and on SEMAINE for dimensional affect estimation, using only still images at test time.

Includes members:
  Member "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild")
  Member "a functional regression approach to facial landmark tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a fast online cascaded regression algorithm for face alignment")
  Member "action anticipation by predicting future dynamic images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "diverse and admissible trajectory forecasting through multimodal context understanding")
  Member "ganimation anatomically aware facial animation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "joint action unit localisation and intensity estimation through heatmap regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatio temporal relation and attention learning for facial action unit detection")
  Member "deep structured learning for facial action unit intensity estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "feafa a well annotated dataset for facial expression analysis and 3d facial animation")
  Member "facial expression recognition using enhanced deep 3d convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "emptransfo a multi head transformer architecture for creating empathetic dialog systems")
  Member "deepcoder semi parametric variational autoencoders for automatic facial action coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reenactgan learning to reenact faces via boundary transfer")

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)