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: "generative convolutional networks for latent fingerprint reconstruction"
  Abstract: Performance of fingerprint recognition depends heavily on the extraction of minutiae points. Enhancement of the fingerprint ridge pattern is thus an essential pre-processing step that noticeably reduces false positive and negative detection rates. A particularly challenging setting is when the fingerprint images are corrupted or partially missing. In this work, we apply generative convolutional networks to denoise visible minutiae and predict the missing parts of the ridge pattern. The proposed enhancement approach is tested as a pre-processing step in combination with several standard feature extraction methods such as MINDTCT, followed by biometric comparison using MCC and BOZORTH3. We evaluate our method on several publicly available latent fingerprint datasets captured using different sensors.

Connected through hyperedge HE-1 (induced by source paper "learning representations of graph data a survey"):
  with members:
    - "fast sequence based embedding with diffusion graphs"
    - "adaptive graph convolutional neural networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "deriving neural architectures from sequence and graph kernels"
    - "inductive representation learning on large graphs"

Connected through hyperedge HE-2 (induced by source paper "fpd m net fingerprint image denoising and inpainting using m net based convolutional neural networks"):
  with members:
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "id preserving generative adversarial network for partial latent fingerprint reconstruction"):
  with members:
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "fingerprint distortion rectification using deep convolutional neural networks"
    - "deep cross polarimetric thermal to visible face recognition"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "perceptual losses for real time style transfer and super resolution"
    - "latent fingerprint minutia extraction using fully convolutional network"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "fingerprint distortion rectification using deep convolutional neural networks"):
  with members:
    - "adam a method for stochastic optimization"

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)