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: "complete the look scene based complementary product recommendation"
  Abstract: Modeling fashion compatibility is challenging due to its complexity and subjectivity. Existing work focuses on predicting compatibility between product images (e.g. an image containing a t-shirt and an image containing a pair of jeans). However, these approaches ignore real-world 'scene' images (e.g. selfies); such images are hard to deal with due to their complexity, clutter, variations in lighting and pose (etc.) but on the other hand could potentially provide key context (e.g. the user's body type, or the season) for making more accurate recommendations. In this work, we propose a new task called 'Complete the Look', which seeks to recommend visually compatible products based on scene images. We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images. Our approach measures compatibility both globally and locally via CNNs and attention mechanisms. Extensive experiments show that our method achieves significant performance gains over alternative systems. Human evaluation and qualitative analysis are also conducted to further understand model behavior. We hope this work could lead to useful applications which link large corpora of real-world scenes with shoppable 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 "basconv aggregating heterogeneous interactions for basket recommendation with graph convolutional neural network"):
  with members:
    - "modeling complementary products and customer preferences with context knowledge for online recommendation"
    - "personalized bundle list recommendation"
    - "jscn joint spectral convolutional network for cross domain recommendation"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "spectral collaborative filtering"
    - "neural collaborative filtering"
    - "item2vec neural item embedding for collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "a large scale deep architecture for personalized grocery basket recommendations"):
  with members:
    - "complementary similarity learning using quadruplet network"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "modeling complementary products and customer preferences with context knowledge for online recommendation"
    - "inferring complementary products from baskets and browsing sessions"
    - "billion scale similarity search with gpus"
    - "item2vec neural item embedding for collaborative filtering"
    - "tensorflow a system for large scale machine learning"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"

Connected through hyperedge HE-4 (induced by source paper "complementary similarity learning using quadruplet network"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "universal sentence encoder"
    - "attention is all you need"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "compatibility family learning for item recommendation and generation"
    - "learning fashion compatibility with bidirectional lstms"
    - "learning compatibility across categories for heterogeneous item recommendation"
    - "inferring networks of substitutable and complementary products"

Connected through hyperedge HE-5 (induced by source paper "human extraction and scene transition utilizing mask r cnn"):
  with members:
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)