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: "fashion forward forecasting visual style in fashion"
  Abstract: What is the future of fashion? Tackling this question from a data-driven vision perspective, we propose to forecast visual style trends before they occur. We introduce the first approach to predict the future popularity of styles discovered from fashion images in an unsupervised manner. Using these styles as a basis, we train a forecasting model to represent their trends over time. The resulting model can hypothesize new mixtures of styles that will become popular in the future, discover style dynamics (trendy vs. classic), and name the key visual attributes that will dominate tomorrow's fashion. We demonstrate our idea applied to three datasets encapsulating 80,000 fashion products sold across six years on Amazon. Results indicate that fashion forecasting benefits greatly from visual analysis, much more than textual or meta-data cues surrounding products.

Connected through hyperedge HE-1 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "toward explainable fashion recommendation"
    - "explainable fashion recommendation a semantic attribute region guided approach"
    - "context aware visual compatibility prediction"
    - "regularizing black box models for improved interpretability"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "outfit compatibility prediction and diagnosis with multi layered comparison network"
    - "learning type aware embeddings for fashion compatibility"

Connected through hyperedge HE-2 (induced by source paper "tailorgan making user defined fashion designs"):
  with members:
    - "lip movements generation at a glance"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "sequential attention gan for interactive image editing via dialogue"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-3 (induced by source paper "fashion minimal edits for outfit improvement"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "learning type aware embeddings for fashion compatibility"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "everybody dance now"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "creating capsule wardrobes from fashion images"

Connected through hyperedge HE-4 (induced by source paper "dressing for diverse body shapes"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "sizenet weakly supervised learning of visual size and fit in fashion images"
    - "fashion retrieval via graph reasoning networks on a similarity pyramid"
    - "3d virtual garment modeling from rgb images"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "360 degree textures of people in clothing from a single image"
    - "multi garment net learning to dress 3d people from images"
    - "a deep learning system for predicting size and fit in fashion e commerce"

Connected through hyperedge HE-5 (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-6 (induced by source paper "fashion iq a new dataset towards retrieving images by natural language feedback"):
  with members:
    - "neural naturalist generating fine grained image comparisons"
    - "robust change captioning"
    - "drill down interactive retrieval of complex scenes using natural language queries"
    - "expressing visual relationships via language"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "learning to describe differences between pairs of similar images"
    - "composing text and image for image retrieval an empirical odyssey"
    - "dialog based interactive image retrieval"

Connected through hyperedge HE-7 (induced by source paper "the fashion iq dataset retrieving images by combining side information and relative natural language feedback"):
  with members:
    - "deepfashion2 a versatile benchmark for detection pose estimation segmentation and re identification of clothing images"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "composing text and image for image retrieval an empirical odyssey"
    - "dialog based interactive image retrieval"
    - "person search with natural language description"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creating capsule wardrobes from fashion images"
    - "towards building large scale multimodal domain aware conversation systems"

Connected through hyperedge HE-8 (induced by source paper "context aware visual compatibility prediction"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "learning type aware embeddings for fashion compatibility"
    - "relational inductive biases deep learning and graph networks"
    - "recommending outfits from personal closet"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "prototypical networks for few shot learning"
    - "creating capsule wardrobes from fashion images"
    - "geometric matrix completion with recurrent multi graph neural networks"

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)