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: "recommending outfits from personal closet"
  Abstract: We consider grading a fashion outfit for recommendation, where we assume that users have a closet of items and we aim at producing a score for an arbitrary combination of items in the closet. The challenge in outfit grading is that the input to the system is a bag of item pictures that are unordered and vary in size. We build a deep neural network-based system that can take variable-length items and predict a score. We collect a large number of outfits from a popular fashion sharing website, Polyvore, and evaluate the performance of our grading system. We compare our model with a random-choice baseline, both on the traditional classification evaluation and on people's judgment using a crowdsourcing platform. With over 84% in classification accuracy and 91% matching ratio to human annotators, our model can reliably grade the quality of an outfit. We also build an outfit recommender on top of our grader to demonstrate the practical application of our model for a personal closet assistant.

Connected through hyperedge HE-1 (induced by source paper "toward explainable fashion recommendation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explainable fashion recommendation with joint outfit matching and comment generation"
    - "smoothgrad removing noise by adding noise"
    - "on calibration of modern neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "streetstyle exploring world wide clothing styles from millions of photos"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-2 (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"
    - "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"
    - "compatibility family learning for item recommendation and generation"

Connected through hyperedge HE-3 (induced by source paper "outfit compatibility prediction and diagnosis with multi layered comparison network"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "non local neural networks"
    - "attention is all you need"
    - "compatibility family learning for item recommendation and generation"
    - "style2vec representation learning for fashion items from style sets"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "learning fashion compatibility with bidirectional lstms"

Connected through hyperedge HE-4 (induced by source paper "deep set to set matching and learning"):
  with members:
    - "group reidentification with multigrained matching and integration"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "learning type aware embeddings for fashion compatibility"
    - "deep sets"
    - "fashionnet personalized outfit recommendation with deep neural network"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-5 (induced by source paper "fashion outfit generation for e commerce"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "fashionnet personalized outfit recommendation with deep neural network"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "class weighted convolutional features for visual instance search"
    - "compatibility family learning for item recommendation and generation"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-6 (induced by source paper "theme matters fashion compatibility learning via theme attention"):
  with members:
    - "fashion outfit generation for e commerce"
    - "learning type aware embeddings for fashion compatibility"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "creating capsule wardrobes from fashion images"
    - "compatibility family learning for item recommendation and generation"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-7 (induced by source paper "complete the look scene based complementary product recommendation"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "do better imagenet models transfer better"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "learning type aware embeddings for fashion compatibility"
    - "creating capsule wardrobes from fashion images"
    - "automatic spatially aware fashion concept discovery"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "compatibility family learning for item recommendation and generation"

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)