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 "dynamically visual disambiguation of keyword based image search"):
  Abstract: Due to the high cost of manual annotation, learning directly from the web has attracted broad attention. One issue that limits their performance is the problem of visual polysemy. To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation. Compared to existing methods, the primary advantage of our approach lies in that our approach can adapt to the dynamic changes in the search results. Our proposed framework consists of two major steps: we first discover and dynamically select the text queries according to the image search results, then we employ the proposed saliency-guided deep multi-instance learning network to remove outliers and learn classification models for visual disambiguation. Extensive experiments demonstrate the superiority of our proposed approach.

Includes members:
  Member "deep representation learning for road detection through siamese network":
    also connected through other hyperedges:
      (none)
  Member "hierarchical long short term concurrent memory for human interaction recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatiotemporal co attention recurrent neural networks for human skeleton motion prediction")
  Member "towards automatic construction of diverse high quality image dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "extracting visual knowledge from the internet making sense of image data")
  Member "exploiting web images for dataset construction a domain robust approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "c rpns promoting object detection in real world via a cascade structure of region proposal networks")
  Member "unsupervised visual sense disambiguation for verbs using multimodal embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning visual actions using multiple verb only labels")
  Member "frankenstein learning deep face representations using small data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning on small datasets without pre training using cosine loss")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "learning deep features for discriminative localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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.IR(Information Retrieval)