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 "end to end image captioning exploits multimodal distributional similarity"):
  Abstract: We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn ‘distributional similarity’ in a multimodal feature space by mapping a test image to similar training images in this space and generating a caption from the same space. To validate our hypothesis, we focus on the ‘image’ side of image captioning, and vary the input image representation but keep the RNN#R##N#text generation component of a CNN-RNN model constant. Our analysis indicates that image captioning models (i) are capable of separating structure from noisy input representations; (ii) suffer virtually no significant performance loss when a high dimensional representation is compressed to a lower dimensional space; (iii) cluster images with similar visual and linguistic information together. Our findings indicate that our distributional similarity hypothesis holds. We conclude that regardless of the image representation used image captioning systems seem to match images and generate captions in a learned joint image-text semantic subspace.

Includes members:
  Member "object counts bringing explicit detections back into image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on architectures for including visual information in neural language models for image description")
  Member "obj2text generating visually descriptive language from object layouts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "image captioning with unseen objects")
  Member "bottom up and top down attention for image captioning and visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "yolo9000 better faster stronger":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "boosting image captioning with attributes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "self critical sequence training for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "language models for image captioning the quirks and what works":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "what value do explicit high level concepts have in vision to language problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")

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)