human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "fusion vectors embedding graph fusions for efficient unsupervised rank aggregation"
  Abstract: The vast increase in amount and complexity of digital content led to a wide interest in ad-hoc retrieval systems in recent years. Complementary, the existence of heterogeneous data sources and retrieval models stimulated the proliferation of increasingly ingenious and effective rank aggregation functions. Although recently proposed rank aggregation functions are promising with respect to effectiveness, existing proposals in the area usually overlook efficiency aspects. We propose an innovative rank aggregation function that is unsupervised, intrinsically multimodal, and targeted for fast retrieval and top effectiveness performance. We introduce the concepts of embedding and indexing of graph-based rank-aggregation representation models, and their application for search tasks. Embedding formulations are also proposed for graph-based rank representations. We introduce the concept of fusion vectors, a late-fusion representation of objects based on ranks, from which an intrinsically rank-aggregation retrieval model is defined. Next, we present an approach for fast retrieval based on fusion vectors, thus promoting an efficient rank aggregation system. Our method presents top effectiveness performance among state-of-the-art related work, while bringing novel aspects of multimodality and effectiveness. Consistent speedups are achieved against the recent baselines in all datasets considered.

Connected through hyperedge HE-1 (induced by source paper "multimodal representation model based on graph based rank fusion"):
  with members:
    - "unsupervised graph based rank aggregation for improved retrieval"
    - "do better imagenet models transfer better"
    - "tensor fusion network for multimodal sentiment analysis"
    - "multimodal machine learning a survey and taxonomy"
    - "learning transferable architectures for scalable image recognition"
    - "image text multi modal representation learning by adversarial backpropagation"
    - "deep residual learning for image recognition"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"

Question: Please tell me which class the center node 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)