human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep representations for cross spectral ocular biometrics"):
  Abstract: One of the major challenges in ocular biometrics is the cross-spectral scenario, i.e. how to match images acquired in different wavelengths. This study designs and extensively evaluates cross-spectral ocular verification methods using well known deep learning representations based on the iris and periocular regions. Using as inputs, the bounding boxes of non-normalised iris–periocular regions, the authors fine-tune convolutional neural network models, originally trained for face recognition. On the basis of the experiments carried out in two publicly available cross-spectral ocular databases, they report results for intra-spectral and cross-spectral scenarios, with the best performance being observed when fusing ResNet-50 deep representations from both the periocular and iris regions. When compared to the state of the art, they observed that the proposed solution consistently reduces the equal error ratevalues by 90%/93%/96% and 61%/77%/83% on the cross-spectral scenario and in the PolyU bi-spectral and cross-eye–cross-spectral datasets. Finally, they evaluate the effect that the ‘deepness’ factor of feature representations has in recognition effectiveness, and based on a subjective analysis of the most problematic pairwise comparisons – they point out further directions for this field of research.

Includes members:
  Member "an efficient and layout independent automatic license plate recognition system based on the yolo detector":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can giraffes become birds an evaluation of image to image translation for data generation")
  Member "simultaneous iris and periocular region detection using coarse annotations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cnn hyperparameter tuning applied to iris liveness detection")
  Member "robust iris segmentation based on fully convolutional networks and generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sip segnet a deep convolutional encoder decoder network for joint semantic segmentation and extraction of sclera iris and pupil based on periocular region suppression")
  Member "a benchmark for iris location and a deep learning detector evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cnn hyperparameter tuning applied to iris liveness detection")
  Member "fully convolutional networks and generative adversarial networks applied to sclera segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sip segnet a deep convolutional encoder decoder network for joint semantic segmentation and extraction of sclera iris and pupil based on periocular region suppression")
  Member "the impact of preprocessing on deep representations for iris recognition on unconstrained environments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectrum translation for cross spectral ocular matching")
  Member "a robust real time automatic license plate recognition based on the yolo detector":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cnn hyperparameter tuning applied to iris liveness detection")
  Member "periocular recognition using cnn features off the shelf":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cross sensor and cross spectral periocular biometrics a comparative benchmark including smartphone authentication")

Question: Please tell me which class the source paper 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)