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: "automatic attribute discovery with neural activations"
  Abstract: How can a machine learn to recognize visual attributes emerging out of online community without a definitive supervised dataset? This paper proposes an automatic approach to discover and analyze visual attributes from a noisy collection of image-text data on the Web. Our approach is based on the relationship between attributes and neural activations in the deep network. We characterize the visual property of the attribute word as a divergence within weakly-annotated set of images. We show that the neural activations are useful for discovering and learning a classifier that well agrees with human perception from the noisy real-world Web data. The empirical study suggests the layered structure of the deep neural networks also gives us insights into the perceptual depth of the given word. Finally, we demonstrate that we can utilize highly-activating neurons for finding semantically relevant regions.

Connected through hyperedge HE-1 (induced by source paper "aesthetic image captioning from weakly labelled photographs"):
  with members:
    - "colornet estimating colorfulness in natural images"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a geometry sensitive approach for photographic style classification"
    - "learning to evaluate image captioning"
    - "neural algebra of classifiers"
    - "convolutional image captioning"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"

Connected through hyperedge HE-2 (induced by source paper "why do these match explaining the behavior of image similarity models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "learning type aware embeddings for fashion compatibility"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "discriminative learning of latent features for zero shot recognition"
    - "a zero shot framework for sketch based image retrieval"

Connected through hyperedge HE-3 (induced by source paper "learning similarity conditions without explicit supervision"):
  with members:
    - "context aware visual compatibility prediction"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "using discriminative methods to learn fashion compatibility across datasets"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "learning fashion compatibility with bidirectional lstms"
    - "fashion forward forecasting visual style in fashion"
    - "learning compatibility across categories for heterogeneous item recommendation"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "u net convolutional networks for biomedical image segmentation"
    - "image based recommendations on styles and substitutes"

Connected through hyperedge HE-5 (induced by source paper "brand logo visual analysis of fashion brands"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "network dissection quantifying interpretability of deep visual representations"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "fashion forward forecasting visual style in fashion"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "fashion landmark detection in the wild"
    - "top down neural attention by excitation backprop"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"

Connected through hyperedge HE-6 (induced by source paper "the devil is in the middle exploiting mid level representations for cross domain instance matching"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "network dissection quantifying interpretability of deep visual representations"
    - "in defense of the triplet loss for person re identification"
    - "deeply learned part aligned representations for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "person re identification by deep joint learning of multi loss classification"
    - "divide and fuse a re ranking approach for person re identification"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-7 (induced by source paper "feedback prop convolutional neural network inference under partial evidence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "network dissection quantifying interpretability of deep visual representations"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "opening the black box of deep neural networks via information"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "layer wise relevance propagation for neural networks with local renormalization layers"
    - "top down neural attention by excitation backprop"
    - "interactive inter layer activeness propagation"

Connected through hyperedge HE-8 (induced by source paper "automatic spatially aware fashion concept discovery"):
  with members:
    - "learning fashion compatibility with bidirectional lstms"
    - "disentangling nonlinear perceptual embeddings with multi query triplet networks"
    - "end to end localization and ranking for relative attributes"
    - "learning deep structure preserving image text embeddings"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "automatic concept discovery from parallel text and visual corpora"
    - "selecting relevant web trained concepts for automated event retrieval"
    - "rethinking the inception architecture for computer vision"

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)