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: "modeling visual compatibility through hierarchical mid level elements"
  Abstract: In this paper we present a hierarchical method to discover mid-level elements with the objective of modeling visual compatibility between objects. At the base-level, our method identifies patterns of CNN activations with the aim of modeling different variations/styles in which objects of the classes of interest may occur. At the top-level, the proposed method discovers patterns of co-occurring activations of base-level elements that define visual compatibility between pairs of object classes. Experiments on the massive Amazon dataset show the strength of our method at describing object classes and the characteristics that drive the compatibility between them.

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 "grid saliency for context explanations of semantic segmentation"):
  with members:
    - "interpreting adversarial examples with attributes"
    - "fast scnn fast semantic segmentation network"
    - "on physical adversarial patches for object detection"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "context proposals for saliency detection"
    - "guided upsampling network for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "improving outfit recommendation with co supervision of fashion generation"):
  with members:
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "aesthetic based clothing recommendation"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "creating capsule wardrobes from fashion images"
    - "visually aware fashion recommendation and design with generative image models"
    - "learning fashion compatibility with bidirectional lstms"
    - "cvae gan fine grained image generation through asymmetric training"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-4 (induced by source paper "recommending outfits from personal closet"):
  with members:
    - "pose guided person image generation"
    - "looking at outfit to parse clothing"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "image based recommendations on styles and substitutes"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "harmonious attention network for person re identification"
    - "deeply learned part aligned representations for person re identification"
    - "deep mutual learning"
    - "camera style adaptation for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "person re identification by deep joint learning of multi loss classification"

Connected through hyperedge HE-6 (induced by source paper "visual explanation by interpretation improving visual feedback capabilities of deep neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "multi agent diverse generative adversarial networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "relating input concepts to convolutional neural network decisions"
    - "interpretable convolutional neural networks"
    - "planet photo geolocation with convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning fashion compatibility with bidirectional lstms"):
  with members:
    - "automatic spatially aware fashion concept discovery"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "a dataset and exploration of models for understanding video data through fill in the blank question answering"
    - "video fill in the blank with merging lstms"
    - "jointly modeling embedding and translation to bridge video and language"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "uncovering temporal context for video question and answering"
    - "image based recommendations on styles and substitutes"

Connected through hyperedge HE-8 (induced by source paper "algorithmic clothing hybrid recommendation from street style to shop"):
  with members:
    - "large scale image analysis using docker sandboxing"
    - "deep tensor encoding"
    - "fashion forward forecasting visual style in fashion"
    - "ssd single shot multibox detector"
    - "r fcn object detection via region based fully convolutional networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

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)