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 "multimodal representation model based on graph based rank fusion"):
  Abstract: This paper proposes an unsupervised representation model, based on rank-fusion graphs, for general applicability in multimodal tasks, either unsupervised or supervised prediction. Rank-fusion graphs encode information from multiple descriptors and retrieval models, thus being able to capture underlying relationships between modalities, samples, and the collection itself. By doing so, our method is able to promote a fusion model better than either early-fusion and late-fusion alternatives. The solution is based on the encoding of multiple ranks of a query, defined according to different criteria, into a graph. Later, we embed the generated graph into a feature space, creating fusion vectors. Those embeddings are employed to build an estimator that infers whether an input (even multimodal) object refers to a class (or event) or not. Performed experiments in the context of multiple multimodal and visual datasets, evaluated on several descriptors and retrieval models, demonstrate that our representation model is highly effective for different detection scenarios involving visual, textual, and multimodal features, yielding better detection results than state-of-the-art methods.

Includes members:
  Member "fusion vectors embedding graph fusions for efficient unsupervised rank aggregation":
    also connected through other hyperedges:
      (none)
  Member "unsupervised graph based rank aggregation for improved retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fusion vectors embedding graph fusions for efficient unsupervised rank aggregation")
  Member "do better imagenet models transfer better":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "tensor fusion network for multimodal sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fgn fusion glyph network for chinese named entity recognition")
  Member "multimodal machine learning a survey and taxonomy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning curiosity algorithms")
  Member "image text multi modal representation learning by adversarial backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "do cross modal systems leverage semantic relationships")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)