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: "understanding trained cnns by indexing neuron selectivity"
  Abstract: The impressive performance and plasticity of convolutional neural networks to solve different vision problems are shadowed by their black-box nature and its consequent lack of full understanding. To reduce this gap we propose to describe the activity of individual neurons by quantifiyng their inherent selectivity to specific properties. Our approach is based on the definition of feature selectivity indexes that allow the ranking of neurons according to specific properties. Here we report the results of exploring selectivity indexes for: (a) an image feature (color); and (b) an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer conv4 or class selective neurons such as dog-face neurons in layer conv5, and establishes a methodology to derive other selectivity properties.  Indexing on neuron selectivity can statistically draw how features and classes are represented through layers at a moment when the size of trained nets is growing and automatic tools to index can be helpful.

Connected through hyperedge HE-1 (induced by source paper "conceptual content in deep convolutional neural networks an analysis into multi faceted properties of neurons"):
  with members:
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "a new method to visualize deep neural networks"
    - "understanding neural networks through deep visualization"
    - "deep learning and the information bottleneck principle"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

Connected through hyperedge HE-2 (induced by source paper "sena cnn overcoming catastrophic forgetting in convolutional neural networks by selective network augmentation"):
  with members:
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "continual learning with deep generative replay"
    - "less forgetting learning in deep neural networks"
    - "learning without forgetting"
    - "blocks and fuel frameworks for deep learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-3 (induced by source paper "a multimodal recommender system for large scale assortment generation in e commerce"):
  with members:
    - "discovering style trends through deep visually aware latent item embeddings"
    - "creating capsule wardrobes from fashion images"
    - "learning compatibility across categories for heterogeneous item recommendation"
    - "image based recommendations on styles and substitutes"
    - "deep residual learning for image recognition"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-4 (induced by source paper "discovering style trends through deep visually aware latent item embeddings"):
  with members:
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "understanding neural networks through deep visualization"
    - "deep residual learning for image recognition"
    - "visualizing and understanding convolutional networks"

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)