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 "reducing geographic performance differential for face recognition"):
  Abstract: As face recognition algorithms become more accurate and get deployed more widely, it becomes increasingly important to ensure that the algorithms work equally well for everyone. We study the geographic performance differentials-differences in false acceptance and false rejection rates across different countries-when comparing selfies against photos from ID documents. We show how to mitigate geographic performance differentials using sampling strategies despite large imbalances in the dataset. Using vanilla domain adaptation strategies to fine-tune a face recognition CNN on domain-specific doc-selfie data improves the performance of the model on such data, but, in the presence of imbalanced training data, also significantly increases the demographic bias. We then show how to mitigate this effect by employing sampling strategies to balance the training procedure.

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 "accuracy comparison across face recognition algorithms where are we on measuring race bias":
    also connected through other hyperedges:
      (none)
  Member "identity document to selfie face matching across adolescence":
    also connected through other hyperedges:
      (none)
  Member "deep learning is robust to massive label noise":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "derivative manipulation for general example weighting")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "docface matching id document photos to selfies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "identity document to selfie face matching across adolescence")
  Member "docface id document to selfie matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "identity document to selfie face matching across adolescence")
  Member "an empirical study of example forgetting during deep neural network learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning and memorizing representative prototypes for 3d point cloud semantic and instance segmentation")

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)