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: "describing natural images containing novel objects with knowledge guided assitance"
  Abstract: Images in the wild encapsulate rich knowledge about varied abstract concepts and cannot be sufficiently described with models built only using image-caption pairs containing selected objects. We propose to handle such a task with the guidance of a knowledge base that incorporate many abstract concepts. Our method is a two-step process where we first build a multi-entity-label image recognition model to predict abstract concepts as image labels and then leverage them in the second step as an external semantic attention and constrained inference in the caption generation model for describing images that depict unseen/novel objects. Evaluations show that our models outperform most of the prior work for out-of-domain captioning on MSCOCO and are useful for integration of knowledge and vision in general.

Connected through hyperedge HE-1 (induced by source paper "captioning images with novel objects via online vocabulary expansion"):
  with members:
    - "image captioning with unseen objects"
    - "large scale long tailed recognition in an open world"
    - "pointing novel objects in image captioning"
    - "neural baby talk"
    - "deep contextualized word representations"
    - "decoupled novel object captioner"
    - "exploring visual relationship for image captioning"
    - "partially supervised image captioning"

Connected through hyperedge HE-2 (induced by source paper "pointing novel objects in image captioning"):
  with members:
    - "neural baby talk"
    - "jointly localizing and describing events for dense video captioning"
    - "decoupled novel object captioner"
    - "exploring visual relationship for image captioning"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "video captioning with transferred semantic attributes"
    - "image captioning with semantic attention"

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)