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: "automated latent fingerprint recognition"
  Abstract: Latent fingerprints are one of the most important and widely used evidence in law enforcement and forensic agencies worldwide. Yet, NIST evaluations show that the performance of state-of-the-art latent recognition systems is far from satisfactory. An automated latent fingerprint recognition system with high accuracy is essential to compare latents found at crime scenes to a large collection of reference prints to generate a candidate list of possible mates. In this paper, we propose an automated latent fingerprint recognition algorithm that utilizes Convolutional Neural Networks (ConvNets) for ridge flow estimation and minutiae descriptor extraction, and extract complementary templates (two minutiae templates and one texture template) to represent the latent. The comparison scores between the latent and a reference print based on the three templates are fused to retrieve a short candidate list from the reference database. Experimental results show that the rank-1 identification accuracies (query latent is matched with its true mate in the reference database) are 64.7% for the NIST SD27 and 75.3% for the WVU latent databases, against a reference database of 100K rolled prints. These results are the best among published papers on latent recognition and competitive with the performance (66.7% and 70.8% rank-1 accuracies on NIST SD27 and WVU DB, respectively) of a leading COTS latent Automated Fingerprint Identification System (AFIS). By score-level (rank-level) fusion of our system with the commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification performance can be improved from 64.7% and 75.3% to 73.3% (74.4%) and 76.6% (78.4%) on NIST SD27 and WVU latent databases, respectively.

Connected through hyperedge HE-1 (induced by source paper "fingerprints fixed length representation via deep networks and domain knowledge"):
  with members:
    - "automatic cropping fingermarks latent fingerprint segmentation"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "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"
    - "multi task cnn model for attribute prediction"

Connected through hyperedge HE-2 (induced by source paper "learning a fixed length fingerprint representation"):
  with members:
    - "automatic latent fingerprint segmentation"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "end to end latent fingerprint search"
    - "secure face matching using fully homomorphic encryption"
    - "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-3 (induced by source paper "end to end latent fingerprint search"):
  with members:
    - "automatic latent fingerprint segmentation"
    - "latent fingerprint recognition role of texture template"
    - "on the intrinsic dimensionality of face representation"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "latent fingerprint recognition role of texture template"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "fingerprint match in box"):
  with members:
    - "raspireader open source fingerprint reader"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-6 (induced by source paper "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"):
  with members:
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "latent fingerprint minutia extraction using fully convolutional network"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

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)