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: "decoupled novel object captioner"
  Abstract: Image captioning is a challenging task where the machine automatically describes an image by sentences or phrases. It often requires a large number of paired image-sentence annotations for training. However, a pre-trained captioning model can hardly be applied to a new domain in which some novel object categories exist, i.e., the objects and their description words are unseen during model training. To correctly caption the novel object, it requires professional human workers to annotate the images by sentences with the novel words. It is labor expensive and thus limits its usage in real-world applications. In this paper, we introduce the zero-shot novel object captioning task where the machine generates descriptions without extra training sentences about the novel object. To tackle the challenging problem, we propose a Decoupled Novel Object Captioner (DNOC) framework that can fully decouple the language sequence model from the object descriptions. DNOC has two components. 1) A Sequence Model with the Placeholder (SM-P) generates a sentence containing placeholders. The placeholder represents an unseen novel object. Thus, the sequence model can be decoupled from the novel object descriptions. 2) A key-value object memory built upon the freely available detection model, contains the visual information and the corresponding word for each object. A query generated from the SM-P is used to retrieve the words from the object memory. The placeholder will further be filled with the correct word, resulting in a caption with novel object descriptions. The experimental results on the held-out MSCOCO dataset demonstrate the ability of DNOC in describing novel concepts.

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"
    - "pointing novel objects in image captioning"

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"
    - "pointing novel objects in image captioning"
    - "neural baby talk"
    - "deep contextualized word representations"
    - "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 "image captioning with unseen objects"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "neural baby talk"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "pointing novel objects in image captioning"):
  with members:
    - "neural baby talk"
    - "jointly localizing and describing events for dense video captioning"
    - "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"
    - "describing natural images containing novel objects with knowledge guided assitance"
    - "video captioning with transferred semantic attributes"
    - "image captioning with semantic attention"

Connected through hyperedge HE-5 (induced by source paper "cascaded revision network for novel object captioning"):
  with members:
    - "neural baby talk"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "text guided attention model for image captioning"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "guided open vocabulary image captioning with constrained beam search"

Connected through hyperedge HE-6 (induced by source paper "nocaps novel object captioning at scale"):
  with members:
    - "evalai towards better evaluation systems for ai agents"
    - "neural baby talk"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "partially supervised image captioning"

Connected through hyperedge HE-7 (induced by source paper "intention oriented image captions with guiding objects"):
  with members:
    - "neural baby talk"
    - "diverse and coherent paragraph generation from images"
    - "improved image captioning via policy gradient optimization of spider"
    - "actor critic sequence training for image captioning"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "creativity generating diverse questions using variational autoencoders"

Connected through hyperedge HE-8 (induced by source paper "learning to compose topic aware mixture of experts for zero shot video captioning"):
  with members:
    - "neural baby talk"
    - "universal neural machine translation for extremely low resource languages"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "quo vadis action recognition a new model and the kinetics dataset"

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)