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 "partially supervised image captioning"):
  Abstract: Image captioning models are becoming increasingly successful at describing the content of images in restricted domains. However, if these models are to function in the wild - for example, as aids for the visually impaired - a much larger number and variety of visual concepts must be understood. In this work, we teach image captioning models new visual concepts with partial supervision, such as available from object detection and image label datasets. As these datasets contain text fragments rather than complete captions, we formulate this problem as learning from incomplete data. To flexibly characterize our uncertainty about the unobserved complete sequence, we represent each incomplete training sequence with its own finite state automaton encoding acceptable complete sequences. We then propose a novel algorithm for training sequence models, such as recurrent neural networks, on incomplete sequences specified in this manner. In the context of image captioning, our method lifts the restriction that previously required image captioning models to be trained on paired image-sentence corpora only, or otherwise required specialized model architectures to take advantage of alternative data modalities. Applying our approach to an existing neural captioning model, we achieve state of the art results on the novel object captioning task using the COCO dataset. We further show that we can train a captioning model to describe new visual concepts from the Open Images dataset while maintaining competitive COCO evaluation scores.

Includes members:
  Member "neural baby talk":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "fast lexically constrained decoding with dynamic beam allocation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards understanding neural machine translation with word importance")
  Member "polyglot semantic parsing in apis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "associating natural language comment and source code entities")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "incorporating copying mechanism in image captioning for learning novel objects":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "lexically constrained decoding for sequence generation using grid beam search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "paraphrase generation with latent bag of words")
  Member "bottom up and top down attention for image captioning and visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "zero shot learning the good the bad and the ugly":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crl class representative learning for image classification")

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)