human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "triplet distillation for deep face recognition"
  Abstract: Convolutional neural networks (CNNs) have achieved a great success in face recognition, which unfortunately comes at the cost of massive computation and storage consumption. Many compact face recognition networks are thus proposed to resolve this problem. Triplet loss is effective to further improve the performance of those compact models. However, it normally employs a fixed margin to all the samples, which neglects the informative similarity structures between different identities. In this paper, we propose an enhanced version of triplet loss, named triplet distillation, which exploits the capability of a teacher model to transfer the similarity information to a small model by adaptively varying the margin between positive and negative pairs. Experiments on LFW, AgeDB, and CPLFW datasets show the merits of our method compared to the original triplet loss.

Connected through hyperedge HE-1 (induced by source paper "margindistillation distillation for margin based softmax"):
  with members:
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "relational knowledge distillation"
    - "learning student networks via feature embedding"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "sphereface deep hypersphere embedding for face recognition"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "deep model compression distilling knowledge from noisy teachers"

Connected through hyperedge HE-2 (induced by source paper "deep geometric knowledge distillation with graphs"):
  with members:
    - "and the bit goes down revisiting the quantization of neural networks"
    - "relational knowledge distillation"
    - "attention based pruning for shift networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learning to prune filters in convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "laplacian networks bounding indicator function smoothness for neural networks robustness"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Question: Please tell me which class the center node 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)