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 type aware embeddings for fashion compatibility"
  Abstract: Outfits in online fashion data are composed of items of many different types (e.g. top, bottom, shoes) that share some stylistic relationship with one another. A representation for building outfits requires a method that can learn both notions of similarity (for example, when two tops are interchangeable) and compatibility (items of possibly different type that can go together in an outfit). This paper presents an approach to learning an image embedding that respects item type, and jointly learns notions of item similarity and compatibility in an end-to-end model. To evaluate the learned representation, we crawled 68,306 outfits created by users on the Polyvore website. Our approach obtains 3-5% improvement over the state-of-the-art on outfit compatibility prediction and fill-in-the-blank tasks using our dataset, as well as an established smaller dataset, while supporting a variety of useful queries.

Connected through hyperedge HE-1 (induced by source paper "learning diverse fashion collocation by neural graph filtering"):
  with members:
    - "context aware visual compatibility prediction"
    - "person re identification with deep similarity guided graph neural network"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "videos as space time region graphs"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "focal loss for dense object detection"
    - "feastnet feature steered graph convolutions for 3d shape analysis"

Connected through hyperedge HE-2 (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"
    - "methods for interpreting and understanding deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network"):
  with members:
    - "cbam convolutional block attention module"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "clothing retrieval with visual attention model"
    - "attention is all you need"
    - "automatic spatially aware fashion concept discovery"
    - "learning fashion compatibility with bidirectional lstms"
    - "exploring human like attention supervision in visual question answering"

Connected through hyperedge HE-4 (induced by source paper "sievenet a unified framework for robust image based virtual try on"):
  with members:
    - "context aware visual compatibility prediction"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "a variational u net for conditional appearance and shape generation"
    - "viton an image based virtual try on network"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deformable gans for pose based human image generation"
    - "improved techniques for training gans"

Connected through hyperedge HE-5 (induced by source paper "compatible and diverse fashion image inpainting"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "design design inspiration from generative networks"
    - "instance level human parsing via part grouping network"
    - "toward characteristic preserving image based virtual try on network"
    - "multimodal unsupervised image to image translation"
    - "language guided fashion image manipulation with feature wise transformations"
    - "a variational u net for conditional appearance and shape generation"

Connected through hyperedge HE-6 (induced by source paper "fashion minimal edits for outfit improvement"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "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"
    - "streetstyle exploring world wide clothing styles from millions of photos"

Connected through hyperedge HE-7 (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-8 (induced by source paper "mixed dimension embeddings with application to memory efficient recommendation systems"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "saec similarity aware embedding compression in recommendation systems"
    - "practice on long sequential user behavior modeling for click through rate prediction"
    - "tensorized embedding layers for efficient model compression"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "block based singular value decomposition approach to matrix factorization for recommender systems"

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)