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: "pointing novel objects in image captioning"
  Abstract: Image captioning has received significant attention with remarkable improvements in recent advances. Nevertheless, images in the wild encapsulate rich knowledge and cannot be sufficiently described with models built on image-caption pairs containing only in-domain objects. In this paper, we propose to address the problem by augmenting standard deep captioning architectures with object learners. Specifically, we present Long Short-Term Memory with Pointing (LSTM-P) --- a new architecture that facilitates vocabulary expansion and produces novel objects via pointing mechanism. Technically, object learners are initially pre-trained on available object recognition data. Pointing in LSTM-P then balances the probability between generating a word through LSTM and copying a word from the recognized objects at each time step in decoder stage. Furthermore, our captioning encourages global coverage of objects in the sentence. Extensive experiments are conducted on both held-out COCO image captioning and ImageNet datasets for describing novel objects, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain an average of 60.9% in F1 score on held-out COCO~dataset.

Connected through hyperedge HE-1 (induced by source paper "learning to learn words from visual scenes"):
  with members:
    - "compositional generalization in image captioning"
    - "few shot representation learning for out of vocabulary words"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "compositional generalization through meta sequence to sequence learning"

Connected through hyperedge HE-2 (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"
    - "neural baby talk"
    - "deep contextualized word representations"
    - "decoupled novel object captioner"
    - "exploring visual relationship for image captioning"
    - "partially supervised image captioning"
    - "incorporating copying mechanism in image captioning for learning novel objects"

Connected through hyperedge HE-3 (induced by source paper "vision and language from visual perception to content creation"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "hierarchy parsing for image captioning"
    - "unsupervised person image generation with semantic parsing transformation"
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "generating multiple objects at spatially distinct locations"
    - "joint discriminative and generative learning for person re identification"
    - "relationship aware spatial perception fusion for realistic scene layout generation"
    - "photo realistic facial details synthesis from single image"

Connected through hyperedge HE-4 (induced by source paper "hierarchy parsing for image captioning"):
  with members:
    - "convolutional auto encoding of sentence topics for image paragraph generation"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "learning to segment every thing"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "mask r cnn"
    - "image captioning with semantic attention"

Connected through hyperedge HE-5 (induced by source paper "constructing dynamic knowledge graph for visual semantic understanding and applications in autonomous robotics"):
  with members:
    - "visual query answering by entity attribute graph matching and reasoning"
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "on exploring undetermined relationships for visual relationship detection"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "improving grounded natural language understanding through human robot dialog"
    - "context and attribute grounded dense captioning"
    - "connecting touch and vision via cross modal prediction"
    - "interactive visual grounding of referring expressions for human robot interaction"

Connected through hyperedge HE-6 (induced by source paper "trimmed action recognition dense captioning events in videos and spatio temporal action localization with focus on activitynet challenge 2019"):
  with members:
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "learning spatio temporal representation with local and global diffusion"
    - "to create what you tell generating videos from captions"
    - "jointly localizing and describing events for dense video captioning"
    - "exploring visual relationship for image captioning"
    - "attention is all you need"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "deformable convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "convolutional auto encoding of sentence topics for image paragraph generation"):
  with members:
    - "neural baby talk"
    - "diverse and coherent paragraph generation from images"
    - "exploring visual relationship for image captioning"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "recurrent topic transition gan for visual paragraph generation"
    - "image captioning with semantic attention"
    - "boosting image captioning with attributes"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"

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)