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: "learning like a child fast novel visual concept learning from sentence descriptions of images"
  Abstract: In this paper, we address the task of learning novel visual concepts, and their interactions with other concepts, from a few images with sentence descriptions. Using linguistic context and visual features, our method is able to efficiently hypothesize the semantic meaning of new words and add them to its word dictionary so that they can be used to describe images which contain these novel concepts. Our method has an image captioning module based on m-RNN with several improvements. In particular, we propose a transposed weight sharing scheme, which not only improves performance on image captioning, but also makes the model more suitable for the novel concept learning task. We propose methods to prevent overfitting the new concepts. In addition, three novel concept datasets are constructed for this new task. In the experiments, we show that our method effectively learns novel visual concepts from a few examples without disturbing the previously learned concepts. The project page is this http URL

Connected through hyperedge HE-1 (induced by source paper "image to language understanding captioning approach"):
  with members:
    - "deep reinforcement learning based image captioning with embedding reward"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "using sentence level lstm language models for script inference"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "from captions to visual concepts and back"

Connected through hyperedge HE-2 (induced by source paper "towards unsupervised image captioning with shared multimodal embeddings"):
  with members:
    - "women also snowboard overcoming bias in captioning models"
    - "neural baby talk"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "unsupervised image captioning"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "unpaired image captioning by language pivoting"

Connected through hyperedge HE-3 (induced by source paper "compositional generalization in image captioning"):
  with members:
    - "the curious case of neural text degeneration"
    - "universal dependency parsing from scratch"
    - "nocaps novel object captioning at scale"
    - "neural baby talk"
    - "assessing composition in sentence vector representations"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rnns implicitly implement tensor product representations"

Connected through hyperedge HE-4 (induced by source paper "aesthetic image captioning from weakly labelled photographs"):
  with members:
    - "colornet estimating colorfulness in natural images"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a geometry sensitive approach for photographic style classification"
    - "learning to evaluate image captioning"
    - "neural algebra of classifiers"
    - "convolutional image captioning"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"

Connected through hyperedge HE-5 (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"
    - "describing natural images containing novel objects with knowledge guided assitance"
    - "video captioning with transferred semantic attributes"

Connected through hyperedge HE-6 (induced by source paper "transfer learning from language models to image caption generators better models may not transfer better"):
  with members:
    - "universal language model fine tuning for text classification"
    - "where to put the image in an image caption generator"
    - "spice semantic propositional image caption evaluation"
    - "re evaluating automatic metrics for image captioning"
    - "unsupervised pretraining for sequence to sequence learning"
    - "how transferable are neural networks in nlp applications"
    - "transfer learning for low resource neural machine translation"
    - "language models for image captioning the quirks and what works"

Connected through hyperedge HE-7 (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"
    - "decoupled novel object captioner"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "semantic aware knowledge preservation for zero shot sketch based image retrieval"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "learning large euclidean margin for sketch based image retrieval"
    - "label refinery improving imagenet classification through label progression"
    - "semantic autoencoder for zero shot learning"
    - "deep sketch hashing fast free hand sketch based image retrieval"

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)