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: "technical report image captioning with semantically similar images"
  Abstract: This report presents our submission to the MS COCO Captioning Challenge 2015. The method uses Convolutional Neural Network activations as an embedding to find semantically similar images. From these images, the most typical caption is selected based on unigram frequencies. Although the method received low scores with automated evaluation metrics and in human assessed average correctness, it is competitive in the ratio of captions which pass the Turing test and which are assessed as better or equal to human captions.

Connected through hyperedge HE-1 (induced by source paper "show and tell lessons learned from the 2015 mscoco image captioning challenge"):
  with members:
    - "language models for image captioning the quirks and what works"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "show attend and tell neural image caption generation with visual attention"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "exploring nearest neighbor approaches for image captioning"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "going deeper with convolutions"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-2 (induced by source paper "spice semantic propositional image caption evaluation"):
  with members:
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "from images to sentences through scene description graphs using commonsense reasoning and knowledge"
    - "language models for image captioning the quirks and what works"
    - "robust subgraph generation improves abstract meaning representation parsing"
    - "show attend and tell neural image caption generation with visual attention"
    - "exploring nearest neighbor approaches for image captioning"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"

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)