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 "towards interpretable face recognition"):
  Abstract: Deep CNNs have been pushing the frontier of visual recognition over past years. Besides recognition accuracy, strong demands in understanding deep CNNs in the research community motivate developments of tools to dissect pre-trained models to visualize how they make predictions. Recent works further push the interpretability in the network learning stage to learn more meaningful representations. In this work, focusing on a specific area of visual recognition, we report our efforts towards interpretable face recognition. We propose a spatial activation diversity loss to learn more structured face representations. By leveraging the structure, we further design a feature activation diversity loss to push the interpretable representations to be discriminative and robust to occlusions. We demonstrate on three face recognition benchmarks that our proposed method is able to improve face recognition accuracy with easily interpretable face representations.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "representation learning by rotating your faces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "on learning 3d face morphable model from in the wild images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "retinaface single stage dense face localisation in the wild")
  Member "gait recognition via disentangled representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hi cmd hierarchical cross modality disentanglement for visible infrared person re identification")
  Member "disentangling features in 3d face shapes for joint face reconstruction and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transferable recognition aware image processing")
  Member "multi task convolutional neural network for pose invariant face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  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")
  Member "multicolumn networks for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distribution distillation loss generic approach for improving face recognition from hard samples")

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)