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: "context aware visual compatibility prediction"
  Abstract: How do we determine whether two or more clothing items are compatible or visually appealing? Part of the answer lies in understanding of visual aesthetics, and is biased by personal preferences shaped by social attitudes, time, and place. In this work we propose a method that predicts compatibility between two items based on their visual features, as well as their context. We define context as the products that are known to be compatible with each of these item. Our model is in contrast to other metric learning approaches that rely on pairwise comparisons between item features alone. We address the compatibility prediction problem using a graph neural network that learns to generate product embeddings conditioned on their context. We present results for two prediction tasks (fill in the blank and outfit compatibility) tested on two fashion datasets Polyvore and Fashion-Gen, and on a subset of the Amazon dataset; we achieve state of the art results when using context information and show how test performance improves as more context is used.

Connected through hyperedge HE-1 (induced by source paper "learning diverse fashion collocation by neural graph filtering"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "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"
    - "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"
    - "methods for interpreting and understanding deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "sievenet a unified framework for robust image based virtual try on"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "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-4 (induced by source paper "learning similarity conditions without explicit supervision"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "give me a hint navigating image databases using human in the loop feedback"
    - "learning two branch neural networks for image text matching tasks"
    - "conditional image text embedding networks"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creating capsule wardrobes from fashion images"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "learning fashion compatibility with bidirectional lstms"

Connected through hyperedge HE-5 (induced by source paper "fashion outfit complementary item retrieval"):
  with members:
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "learning similarity conditions without explicit supervision"
    - "learning type aware embeddings for fashion compatibility"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "compatibility family learning for item recommendation and generation"
    - "visually aware fashion recommendation and design with generative image models"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-6 (induced by source paper "estimating early fundraising performance of innovations via graph based market environment model"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "gated graph sequence neural networks"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

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)