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: "automatic latent fingerprint segmentation"
  Abstract: We present a simple but effective method for automatic latent fingerprint segmentation, called SegFinNet. SegFinNet takes a latent image as an input and outputs a binary mask highlighting the friction ridge pattern. Our algorithm combines fully convolutional neural network and detection-based approaches to process the entire input latent image in one shot instead of using latent patches. Experimental results on three different latent databases (i.e. NIST SD27, WVU, and an operational forensic database) show that SegFinNet outperforms both human markup for latents and the state-of-the-art latent segmentation algorithms. We further show that this improved cropping boosts the hit rate of a latent fingerprint matcher.

Connected through hyperedge HE-1 (induced by source paper "learning a fixed length fingerprint representation"):
  with members:
    - "multi task convolutional neural network for pose invariant face recognition"
    - "end to end latent fingerprint search"
    - "secure face matching using fully homomorphic encryption"
    - "automated latent fingerprint recognition"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "spatial transformer networks"

Connected through hyperedge HE-2 (induced by source paper "end to end pore extraction and matching in latent fingerprints going beyond minutiae"):
  with members:
    - "fingerprints fixed length representation via deep networks and domain knowledge"
    - "infant prints fingerprints for reducing infant mortality"
    - "end to end latent fingerprint search"
    - "automatic dataset annotation to learn cnn pore description for fingerprint recognition"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"

Connected through hyperedge HE-3 (induced by source paper "end to end latent fingerprint search"):
  with members:
    - "latent fingerprint recognition role of texture template"
    - "on the intrinsic dimensionality of face representation"
    - "automated latent fingerprint recognition"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "latent fingerprint minutia extraction using fully convolutional network"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable 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)