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: "an analysis of the connections between layers of deep neural networks"
  Abstract: We present an analysis of different techniques for selecting the connection be- tween layers of deep neural networks. Traditional deep neural networks use ran- dom connection tables between layers to keep the number of connections small and tune to different image features. This kind of connection performs adequately in supervised deep networks because their values are refined during the training. On the other hand, in unsupervised learning, one cannot rely on back-propagation techniques to learn the connections between layers. In this work, we tested four different techniques for connecting the first layer of the network to the second layer on the CIFAR and SVHN datasets and showed that the accuracy can be im- proved up to 3% depending on the technique used. We also showed that learning the connections based on the co-occurrences of the features does not confer an advantage over a random connection table in small networks. This work is helpful to improve the efficiency of connections between the layers of unsupervised deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "convolutional clustering for unsupervised learning"):
  with members:
    - "semi supervised learning with ladder networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "semi supervised learning with deep generative models"
    - "deep metric learning using triplet network"
    - "an analysis of unsupervised pre training in light of recent advances"
    - "visualizing and understanding convolutional networks"
    - "network in network"

Connected through hyperedge HE-2 (induced by source paper "flattened convolutional neural networks for feedforward acceleration"):
  with members:
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "speeding up convolutional neural networks with low rank expansions"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"
    - "caffe convolutional architecture for fast feature embedding"
    - "compressing deep convolutional networks using vector quantization"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-3 (induced by source paper "fracking deep convolutional image descriptors"):
  with members:
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "convolutional neural networks applied to house numbers digit classification"

Connected through hyperedge HE-4 (induced by source paper "committees of deep feedforward networks trained with few data"):
  with members:
    - "unsupervised feature learning by augmenting single images"

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)