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: "what do we understand about convolutional networks"
  Abstract: This document will review the most prominent proposals using multilayer convolutional architectures. Importantly, the various components of a typical convolutional network will be discussed through a review of different approaches that base their design decisions on biological findings and/or sound theoretical bases. In addition, the different attempts at understanding ConvNets via visualizations and empirical studies will be reviewed. The ultimate goal is to shed light on the role of each layer of processing involved in a ConvNet architecture, distill what we currently understand about ConvNets and highlight critical open problems.

Connected through hyperedge HE-1 (induced by source paper "a novel database of children s spontaneous facial expressions liris cse"):
  with members:
    - "deep meta learning learning to learn in the concept space"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "real time convolutional neural networks for emotion and gender classification"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "opening the black box of deep learning"):
  with members:
    - "decoupled networks"
    - "a mathematical framework for superintelligent machines"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "large scale distributed neural network training through online distillation"
    - "deep learning a critical appraisal"
    - "pooling is neither necessary nor sufficient for appropriate deformation stability in cnns"
    - "mathematics of deep learning"
    - "deep learning and quantum entanglement fundamental connections with implications to network design"

Connected through hyperedge HE-3 (induced by source paper "falcon a fourier transform based approach for fast and secure convolutional neural network predictions"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "gazelle a low latency framework for secure neural network inference"
    - "machine learning models that remember too much"
    - "deepsecure scalable provably secure deep learning"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-4 (induced by source paper "an extensive review on spectral imaging in biometric systems challenges and advancements"):
  with members:
    - "a novel database of children s spontaneous facial expressions liris cse"
    - "deep cross polarimetric thermal to visible face recognition"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "deep perceptual mapping for cross modal face recognition"
    - "image level classification in hyperspectral images using feature descriptors with application to face recognition"
    - "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding"
    - "deep perceptual mapping for thermal to visible face recognition"
    - "understanding deep image representations by inverting them"

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)