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: "hierarchical deep learning architecture for 10k objects classification"
  Abstract: Evolution of visual object recognition architectures based on Convolutional Neural Networks & Convolutional Deep Belief Networks paradigms has revolutionized artificial Vision Science. These architectures extract & learn the real world hierarchical visual features utilizing supervised & unsupervised learning approaches respectively. Both the approaches yet cannot scale up realistically to provide recognition for a very large number of objects as high as 10K. We propose a two level hierarchical deep learning architecture inspired by divide & conquer principle that decomposes the large scale recognition architecture into root & leaf level model architectures. Each of the root & leaf level models is trained exclusively to provide superior results than possible by any 1-level deep learning architecture prevalent today. The proposed architecture classifies objects in two steps. In the first step the root level model classifies the object in a high level category. In the second step, the leaf level recognition model for the recognized high level category is selected among all the leaf models. This leaf level model is presented with the same input object image which classifies it in a specific category. Also we propose a blend of leaf level models trained with either supervised or unsupervised learning approaches. Unsupervised learning is suitable whenever labelled data is scarce for the specific leaf level models. Currently the training of leaf level models is in progress; where we have trained 25 out of the total 47 leaf level models as of now. We have trained the leaf models with the best case top-5 error rate of 3.2% on the validation data set for the particular leaf models. Also we demonstrate that the validation error of the leaf level models saturates towards the above mentioned accuracy as the number of epochs are increased to more than sixty.

Connected through hyperedge HE-1 (induced by source paper "indoor image representation by high level semantic features"):
  with members:
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "locally supervised deep hybrid model for scene recognition"
    - "a discriminative representation of convolutional features for indoor scene recognition"
    - "rethinking the inception architecture for computer vision"
    - "multi scale orderless pooling of deep convolutional activation features"

Connected through hyperedge HE-2 (induced by source paper "multi label multi task deep learning for behavioral coding"):
  with members:
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "adversarial multi task learning for text classification"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "theano new features and speed improvements"

Connected through hyperedge HE-3 (induced by source paper "infrared colorization using deep convolutional neural networks"):
  with members:
    - "rapid exact signal scanning with deep convolutional neural networks"
    - "learning representations for automatic colorization"
    - "colorful image colorization"
    - "a theory for rapid exact signal scanning with deep multi scale convolutional neural networks"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "robust deep learning based road prediction for augmented reality navigation systems"):
  with members:
    - "rapid exact signal scanning with deep convolutional neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "fully convolutional networks for semantic segmentation"

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)