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 "the sound of motions"):
  Abstract: Sounds originate from object motions and vibrations of surrounding air. Inspired by the fact that humans is capable of interpreting sound sources from how objects move visually, we propose a novel system that explicitly captures such motion cues for the task of sound localization and separation. Our system is composed of an end-to-end learnable model called Deep Dense Trajectory (DDT), and a curriculum learning scheme. It exploits the inherent coherence of audio-visual signals from a large quantities of unlabeled videos. Quantitative and qualitative evaluations show that comparing to previous models that rely on visual appearance cues, our motion based system improves performance in separating musical instrument sounds. Furthermore, it separates sound components from duets of the same category of instruments, a challenging problem that has not been addressed before.

Includes members:
  Member "creating a multitrack classical music performance dataset for multimodal music analysis challenges insights and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cascade attention guided residue learning gan for cross modal translation")
  Member "self supervised audio visual co segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised learning by cross modal audio video clustering")
  Member "learning to localize sound source in visual scenes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "end to end learning of motion representation for video understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dmc net generating discriminative motion cues for fast compressed video action recognition")
  Member "seeing voices and hearing faces cross modal biometric matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised any to many audiovisual synthesis via exemplar autoencoders")
  Member "talking face generation by adversarially disentangled audio visual representation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "looking to listen at the cocktail party a speaker independent audio visual model for speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "self supervised generation of spatial audio for 360 video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "listen to look action recognition by previewing audio")

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)