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 "relevant features for gender classification in nir periocular images"):
  Abstract: Most gender classifications methods from NIR images have used iris information. Recent work has explored the use of the whole periocular iris region which has surprisingly achieve better results. This suggests the most relevant information for gender classification is not located in the iris as expected. In this work, we analyze and demonstrate the location of the most relevant features that describe gender in periocular NIR images and evaluate its influence its classification. Experiments show that the periocular region contains more gender information than the iris region. We extracted several features (intensity, texture, and shape) and classified them according to its relevance using the XgBoost algorithm. Support Vector Machine and nine ensemble classifiers were used for testing gender accuracy when using the most relevant features. The best classification results were obtained when 4,000 features located on the periocular region were used (89.22\%). Additional experiments with the full periocular iris images versus the iris-Occluded images were performed. The gender classification rates obtained were 84.35\% and 85.75\% respectively. We also contribute to the state of the art with a new database (UNAB-Gender). From results, we suggest focussing only on the surrounding area of the iris. This allows us to realize a faster classification of gender from NIR periocular images.

Includes members:
  Member "predicting gender from iris texture may be harder than it seems":
    also connected through other hyperedges:
      (none)
  Member "gender and ethnicity classification of iris images using deep class encoder":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gender classification from iris texture images using a new set of binary statistical image features")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "iris codes classification using discriminant and witness directions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "examples of artificial perceptions in optical character recognition and iris recognition")
  Member "pigment melanin pattern for iris recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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")

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)