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 "how does gender balance in training data affect face recognition accuracy"):
  Abstract: Even though deep learning methods have greatly increased the overall accuracy of face recognition, an old problem still persists: accuracy is higher for men than for women. Previous researchers have speculated that the difference could be due to cosmetics, head pose, or hair covering the face. It is also often speculated that the lower accuracy for women is caused by women being under-represented in the training data. This work aims to investigate if gender imbalance in the training data is actually the cause of lower accuracy for females. Using a state-of-the-art deep CNN, three different loss functions, and two training datasets, we train each on seven subsets with different male/female ratios, totaling forty two train-ings. The trained face matchers are then tested on three different testing datasets. Results show that gender-balancing the dataset has an overall positive effect, with higher accuracy for most of the combinations of loss functions and datasets when a balanced subset is used. However, for the best combination of loss function and dataset, the original training dataset shows better accuracy on 3 out of 4 times. We observe that test accuracy for males is higher when the training data is all male. However, test accuracy for females is not maximized when the training data is all female. Fora number of combinations of loss function and test dataset, accuracy for females is higher when only 75% of the train-ing data is female than when 100% of the training data is female. This suggests that lower accuracy for females is nota simple result of the fraction of female training data. By clustering face features, we show that in general, male faces are closer to other male faces than female faces, and female faces are closer to other female faces than male faces

Includes members:
  Member "analysis of gender inequality in face recognition accuracy":
    also connected through other hyperedges:
      (none)
  Member "does face recognition accuracy get better with age deep face matchers say no":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis of gender inequality in face recognition accuracy")
  Member "an experimental evaluation of covariates effects on unconstrained face verification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "probabilistic face embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalizing face representation with unlabeled data")
  Member "characterizing the variability in face recognition accuracy relative to race":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "retinaface single stage dense face localisation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalizing face representation with unlabeled data")
  Member "the devil of face recognition is in the noise":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)