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 "learning metrics from teachers compact networks for image embedding"):
  Abstract: Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to use network distillation to efficiently compute image embeddings with small networks. Network distillation has been successfully applied to improve image classification, but has hardly been explored for metric learning. To do so, we propose two new loss functions that model the communication of a deep teacher network to a small student network. We evaluate our system in several datasets, including CUB-200-2011, Cars-196, Stanford Online Products and show that embeddings computed using small student networks perform significantly better than those computed using standard networks of similar size. Results on a very compact network (MobileNet-0.25), which can be used on mobile devices, show that the proposed method can greatly improve Recall@1 results from 27.5\% to 44.6\%. Furthermore, we investigate various aspects of distillation for embeddings, including hint and attention layers, semi-supervised learning and cross quality distillation. (Code is available at this https URL.)

Includes members:
  Member "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "metric learning for novelty and anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi class novelty detection using mix up technique")
  Member "learning deep representations with probabilistic knowledge transfer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "supermix supervising the mixing data augmentation")
  Member "deep mutual learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "knowledge concentration learning 100k object classifiers in a single cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trained model fusion for object detection using gating network")
  Member "darkrank accelerating deep metric learning via cross sample similarities transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "rankiqa learning from rankings for no reference image quality assessment":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment")

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)