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 "see hear and read deep aligned representations"):
  Abstract: We capitalize on large amounts of readily-available, synchronous data to learn a deep discriminative representations shared across three major natural modalities: vision, sound and language. By leveraging over a year of sound from video and millions of sentences paired with images, we jointly train a deep convolutional network for aligned representation learning. Our experiments suggest that this representation is useful for several tasks, such as cross-modal retrieval or transferring classifiers between modalities. Moreover, although our network is only trained with image+text and image+sound pairs, it can transfer between text and sound as well, a transfer the network never observed during training. Visualizations of our representation reveal many hidden units which automatically emerge to detect concepts, independent of the modality.

Includes members:
  Member "ambient sound provides supervision for visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words")
  Member "visual genome connecting language and vision using crowdsourced dense image annotations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "learning aligned cross modal representations from weakly aligned data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space")
  Member "soundnet learning sound representations from unlabeled video":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "the new data and new challenges in multimedia research":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large scale weakly supervised pre training for video action recognition")
  Member "aligning books and movies towards story like visual explanations by watching movies and reading books":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "cross modal distillation for supervision transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "autofoley artificial synthesis of synchronized sound tracks for silent videos with deep learning")
  Member "visually indicated sounds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")

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)