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: "netizen style commenting on fashion photos dataset and diversity measures"
  Abstract: Recently, deep neural network models have achieved promising results in image captioning task. Yet, "vanilla" sentences, only describing shallow appearances (e.g., types, colors), generated by current works are not satisfied netizen style resulting in lacking engagements, contexts, and user intentions. To tackle this problem, we propose Netizen Style Commenting (NSC), to automatically generate characteristic comments to a user-contributed fashion photo. We are devoted to modulating the comments in a vivid "netizen" style which reflects the culture in a designated social community and hopes to facilitate more engagement with users. In this work, we design a novel framework that consists of three major components: (1) We construct a large-scale clothing dataset named NetiLook, which contains 300K posts (photos) with 5M comments to discover netizen-style comments. (2) We propose three unique measures to estimate the diversity of comments. (3) We bring diversity by marrying topic models with neural networks to make up the insufficiency of conventional image captioning works. Experimenting over Flickr30k and our NetiLook datasets, we demonstrate our proposed approaches benefit fashion photo commenting and improve image captioning tasks both in accuracy and diversity.

Connected through hyperedge HE-1 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "end to end deep kronecker product matching for person re identification"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "person re identification with deep similarity guided graph neural network"
    - "videos as space time region graphs"

Connected through hyperedge HE-2 (induced by source paper "generating diverse and informative natural language fashion feedback"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "ssd single shot multibox detector"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "self critical sequence training for image captioning"
    - "language models for image captioning the quirks and what works"

Connected through hyperedge HE-3 (induced by source paper "generating diverse and accurate visual captions by comparative adversarial learning"):
  with members:
    - "rational recurrences"
    - "discriminability objective for training descriptive captions"
    - "diverse and coherent paragraph generation from images"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "speaking the same language matching machine to human captions by adversarial training"

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)