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 "accuracy comparison across face recognition algorithms where are we on measuring race bias"):
  Abstract: Previous generations of face recognition algorithms differ in accuracy for faces of different races (race bias). Whether deep convolutional neural networks (DCNNs) are race biased is less studied. To measure race bias in algorithms, it is important to consider the underlying factors. Here, we present the possible underlying factors and methodological considerations for assessing race bias in algorithms. We investigate data-driven and scenario modeling factors. Data-driven factors include image quality, image population statistics, and algorithm architecture. Scenario modeling considers the role of the "user" of the algorithm (e.g., threshold decisions and demographic constraints). To illustrate how these issues apply, we present data from four face recognition algorithms (one pre- DCNN, three DCNN) for Asian and Caucasian faces. First, for all four algorithms, the degree of bias varied depending on the identification decision threshold. Second, for all algorithms, to achieve equal false accept rates (FARs), Asian faces required higher identification thresholds than Caucasian faces. Third, dataset difficulty affected both overall recognition accuracy and race bias. Fourth, demographic constraints on the formulation of the distributions used in the test, impacted estimates of algorithm accuracy. We conclude with a recommended checklist for measuring race bias in face recognition algorithms.

Includes members:
  Member "characterizing the variability in face recognition accuracy relative to race":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "debface de biasing face recognition":
    also connected through other hyperedges:
      (none)
  Member "a fast and accurate system for face detection identification and verification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalizing face representation with unlabeled data")
  Member "l2 constrained softmax loss for discriminative face verification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalizing face representation with unlabeled data")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")

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)