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 "cross sensor and cross spectral periocular biometrics a comparative benchmark including smartphone authentication"):
  Abstract: The massive availability of cameras results in a wide variability of imaging conditions, producing large intra-class variations and a significant performance drop if images from heterogeneous environments are compared for person recognition. However, as biometrics is deployed, it will be common to replace damaged or obsolete hardware, or to exchange information between applications in heterogeneous environments. Variations in spectral bands can also occur. For example, faces are typically acquired in the visible (VIS) spectrum, while iris is captured in near-infrared (NIR). However, cross-spectrum comparison may be needed if a face from a surveillance camera needs to be compared against a legacy iris database. Here, we propose a multialgorithmic approach to cope with periocular images from different sensors. We integrate different comparators with a fusion scheme based on linear logistic regression, in which scores tend to be log-likelihood ratios. This allows easy interpretation of output scores and the use of Bayes thresholds for optimal decision-making, since scores from different comparators are in the same probabilistic range. We evaluate our approach in the context of the Cross-Eyed Competition, whose aim was to compare recognition approaches when NIR and VIS periocular images are matched. Our approach achieves reductions in error rates of up to 30-40% in cross-spectral NIR-VIS comparisons, leading to EER=0.2% and FRR of just 0.47% at FAR=0.01%, representing the best overall approach of the competition. Experiments are also reported with a database of VIS images from different smartphones. We also discuss the impact of template size and computation times, with the most computationally heavy comparator playing an important role in the results. Lastly, the proposed method is shown to outperform other popular fusion approaches, such as the average of scores, SVMs or Random Forest.

Includes members:
  Member "a survey on periocular biometrics research":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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 "periocular recognition using cnn features off the shelf":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep representations for cross spectral ocular biometrics")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "cnn features off the shelf an astounding baseline for recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

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)