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: "unsupervised learning using pretrained cnn and associative memory bank"
  Abstract: Deep Convolutional features extracted from a comprehensive labeled dataset, contain substantial representations which could be effectively used in a new domain. Despite the fact that generic features achieved good results in many visual tasks, fine-tuning is required for pretrained deep CNN models to be more effective and provide state-of-the-art performance. Fine tuning using the backpropagation algorithm in a supervised setting, is a time and resource consuming process. In this paper, we present a new architecture and an approach for unsupervised object recognition that addresses the above mentioned problem with fine tuning associated with pretrained CNN-based supervised deep learning approaches while allowing automated feature extraction. Unlike existing works, our approach is applicable to general object recognition tasks. It uses a pretrained (on a related domain) CNN model for automated feature extraction pipelined with a Hopfield network based associative memory bank for storing patterns for classification purposes. The use of associative memory bank in our framework allows eliminating backpropagation while providing competitive performance on an unseen dataset.

Connected through hyperedge HE-1 (induced by source paper "context aware design of cyber physical human systems cphs"):
  with members:
    - "improving prediction accuracy in building performance models using generative adversarial networks gans"
    - "improving route choice models by incorporating contextual factors via knowledge distillation"
    - "why do you take that route"
    - "progressively growing generative adversarial networks for high resolution semantic segmentation of satellite images"
    - "cactusnets layer applicability as a metric for transfer learning"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deepsat a learning framework for satellite imagery"

Connected through hyperedge HE-2 (induced by source paper "improving route choice models by incorporating contextual factors via knowledge distillation"):
  with members:
    - "learning efficient detector with semi supervised adaptive distillation"
    - "low resolution face recognition in the wild via selective knowledge distillation"
    - "pixel level reconstruction and classification for noisy handwritten bangla characters"
    - "deep mutual learning"
    - "safernet safe transportation routing in the era of internet of vehicles and mobile crowd sensing"
    - "deepsat a learning framework for satellite imagery"

Connected through hyperedge HE-3 (induced by source paper "deep learning based feature aware data modeling for complex physics simulations"):
  with members:
    - "residual dense network for image super resolution"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "a generative model for volume rendering"

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)