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 "weakly supervised representation learning for unsynchronized audio visual events"):
  Abstract: Audio-visual representation learning is an important task from the perspective of designing machines with the ability to understand complex events. To this end, we propose a novel multimodal framework that instantiates multiple instance learning. We show that the learnt representations are useful for classifying events and localizing their characteristic audio-visual elements. The system is trained using only video-level event labels without any timing information. An important feature of our method is its capacity to learn from unsynchronized audio-visual events. We achieve state-of-the-art results on a large-scale dataset of weakly-labeled audio event videos. Visualizations of localized visual regions and audio segments substantiate our system's efficacy, especially when dealing with noisy situations where modality-specific cues appear asynchronously.

Includes members:
  Member "objects that sound":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi modal domain adaptation for fine grained action recognition")
  Member "surrey cvssp system for dcase2017 challenge task 4":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sound event detection with depthwise separable and dilated convolutions")
  Member "sound event detection using spatial features and convolutional recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sound event detection of weakly labelled data with cnn transformer and automatic threshold optimization")
  Member "see hear and read deep aligned representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multimodal matching transformer for live commenting")
  Member "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cure dataset ladder networks for audio event classification")
  Member "ambient sound provides supervision for visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "seed expand and constrain three principles for weakly supervised image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "audio event detection using weakly labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "limitations of weak labels for embedding and tagging")

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)