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: "who are the devils wearing prada in new york city"
  Abstract: Fashion is a perpetual topic in human social life, and the mass has the penchant to emulate what large city residents and celebrities wear. Undeniably, New York City is such a bellwether large city with all kinds of fashion leadership. Consequently, to study what the fashion trends are during this year, it is very helpful to learn the fashion trends of New York City. Discovering fashion trends in New York City could boost many applications such as clothing recommendation and advertising. Does the fashion trend in the New York Fashion Show actually influence the clothing styles on the public? To answer this question, we design a novel system that consists of three major components: (1) constructing a large dataset from the New York Fashion Shows and New York street chic in order to understand the likely clothing fashion trends in New York, (2) utilizing a learning-based approach to discover fashion attributes as the representative characteristics of fashion trends, and (3) comparing the analysis results from the New York Fashion Shows and street-chic images to verify whether the fashion shows have actual influence on the people in New York City. Through the preliminary experiments over a large clothing dataset, we demonstrate the effectiveness of our proposed system, and obtain useful insights on fashion trends and fashion influence.

Connected through hyperedge HE-1 (induced by source paper "hard aware fashion attribute classification"):
  with members:
    - "msg gan multi scale gradient gan for stable image synthesis"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "imbalanced deep learning by minority class incremental rectification"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "in defense of the triplet loss for person re identification"
    - "progressive growing of gans for improved quality stability and variation"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-2 (induced by source paper "netizen style commenting on fashion photos dataset and diversity measures"):
  with members:
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "text guided attention model for image captioning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "smart reply automated response suggestion for email"
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "captioning images with diverse objects"
    - "image captioning with deep bidirectional lstms"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-3 (induced by source paper "mining fashion outfit composition using an end to end deep learning approach on set data"):
  with members:
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "deep residual learning for image recognition"
    - "user curated image collections modeling and recommendation"
    - "describing multimedia content using attention based encoder decoder networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "network in network"

Connected through hyperedge HE-4 (induced by source paper "fashion forward forecasting visual style in fashion"):
  with members:
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "automatic attribute discovery with neural activations"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "when was that made"
    - "fashion dna merging content and sales data for recommendation and article mapping"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "image based recommendations on styles and substitutes"
    - "cross domain image retrieval with a dual attribute aware ranking network"

Connected through hyperedge HE-5 (induced by source paper "when fashion meets big data discriminative mining of best selling clothing features"):
  with members:
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)