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 "content based video music retrieval using soft intra modal structure constraint"):
  Abstract: Up to now, only limited research has been conducted on cross-modal retrieval of suitable music for a specified video or vice versa. Moreover, much of the existing research relies on metadata such as keywords, tags, or associated description that must be individually produced and attached posterior. This paper introduces a new content-based, cross-modal retrieval method for video and music that is implemented through deep neural networks. We train the network via inter-modal ranking loss such that videos and music with similar semantics end up close together in the embedding space. However, if only the inter-modal ranking constraint is used for embedding, modality-specific characteristics can be lost. To address this problem, we propose a novel soft intra-modal structure loss that leverages the relative distance relationship between intra-modal samples before embedding. We also introduce reasonable quantitative and qualitative experimental protocols to solve the lack of standard protocols for less-mature video-music related tasks. Finally, we construct a large-scale 200K video-music pair benchmark. All the datasets and source code can be found in our online repository (this https URL).

Includes members:
  Member "learning two branch neural networks for image text matching tasks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "sspp dan deep domain adaptation network for face recognition with single sample per person":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dual triplet metric learning for unsupervised domain adaptation in video based face recognition")
  Member "recognizing dynamic scenes with deep dual descriptor based on key frames and key segments":
    also connected through other hyperedges:
      (none)
  Member "beyond deep residual learning for image restoration persistent homology guided manifold simplification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi level wavelet convolutional neural networks")
  Member "image text multi modal representation learning by adversarial backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "do cross modal systems leverage semantic relationships")
  Member "youtube 8m a large scale video classification benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition")
  Member "deep pyramidal residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "convolutional recurrent neural networks for music classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "music2dance dancenet for music driven dance generation")

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)