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: "pixel level reconstruction and classification for noisy handwritten bangla characters"
  Abstract: Classification techniques for images of handwritten characters are susceptible to noise. Quadtrees can be an efficient representation for learning from sparse features. In this paper, we improve the effectiveness of probabilistic quadtrees by using a pixel level classifier to extract the character pixels and remove noise from handwritten character images. The pixel level denoiser (a deep belief network) uses the map responses obtained from a pretrained CNN as features for reconstructing the characters eliminating noise. We experimentally demonstrate the effectiveness of our approach by reconstructing and classifying a noisy version of handwritten Bangla Numeral and Basic Character datasets.

Connected through hyperedge HE-1 (induced by source paper "pcgan char progressively trained classifier generative adversarial networks for classification of noisy handwritten bangla characters"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "cactusnets layer applicability as a metric for transfer learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "context encoders feature learning by inpainting"
    - "low shot visual recognition by shrinking and hallucinating features"
    - "deepsat a learning framework for satellite imagery"
    - "how transferable are features in deep neural networks"

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"
    - "unsupervised learning using pretrained cnn and associative memory bank"
    - "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"

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)