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 "animating face using disentangled audio representations"):
  Abstract: All previous methods for audio-driven talking head generation assume the input audio to be clean with a neutral tone. As we show empirically, one can easily break these systems by simply adding certain background noise to the utterance or changing its emotional tone (to such as sad). To make talking head generation robust to such variations, we propose an explicit audio representation learning framework that disentangles audio sequences into various factors such as phonetic content, emotional tone, background noise and others. We conduct experiments to validate that conditioned on disentangled content representation, the generated mouth movement by our model is significantly more accurate than previous approaches (without disentangled learning) in the presence of noise and emotional variations. We further demonstrate that our framework is compatible with current state-of-the-art approaches by replacing their original audio learning component with ours. To our best knowledge, this is the first work which improves the performance of talking head generation from disentangled audio representation perspective, which is important for many real-world applications.

Includes members:
  Member "capture learning and synthesis of 3d speaking styles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "photorealistic lip sync with adversarial temporal convolutional networks")
  Member "the conversation deep audio visual speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "audio visual speech enhancement using conditional variational auto encoder")
  Member "lip movements generation at a glance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "talking face generation by adversarially disentangled audio visual representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "lrs3 ted a large scale dataset for visual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mutual information maximization for effective lip reading")
  Member "looking to listen at the cocktail party a speaker independent audio visual model for speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "visemenet audio driven animator centric speech animation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "text based editing of talking head video")
  Member "speech driven facial reenactment using conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "high resolution talking face generation via mutual information approximation")

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)