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: "in teacher we trust learning compressed models for pedestrian detection"
  Abstract: Deep convolutional neural networks continue to advance the state-of-the-art in many domains as they grow bigger and more complex. It has been observed that many of the parameters of a large network are redundant, allowing for the possibility of learning a smaller network that mimics the outputs of the large network through a process called Knowledge Distillation. We show, however, that standard Knowledge Distillation is not effective for learning small models for the task of pedestrian detection. To improve this process, we introduce a higher-dimensional hint layer to increase information flow. We also estimate the variance in the outputs of the large network and propose a loss function to incorporate this uncertainty. Finally, we attempt to boost the complexity of the small network without increasing its size by using as input hand-designed features that have been demonstrated to be effective for pedestrian detection. We succeed in training a model that contains $400\times$ fewer parameters than the large network while outperforming AlexNet on the Caltech Pedestrian Dataset.

Connected through hyperedge HE-1 (induced by source paper "knowledge distillation for end to end person search"):
  with members:
    - "learning context graph for person search"
    - "query guided end to end person search"
    - "person search by multi scale matching"
    - "model compression via distillation and quantization"
    - "attention aware compositional network for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "maximum margin metric learning over discriminative nullspace for person re identification"
    - "person search via a mask guided two stream cnn model"

Connected through hyperedge HE-2 (induced by source paper "towards generalizing sensorimotor control across weather conditions"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "multimodal unsupervised image to image translation"
    - "on evaluation of embodied navigation agents"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "on offline evaluation of vision based driving models"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "modular vehicle control for transferring semantic information to unseen weather conditions using gans"

Connected through hyperedge HE-3 (induced by source paper "distilling pixel wise feature similarities for semantic segmentation"):
  with members:
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "improving fast segmentation with teacher student learning"
    - "an analysis of deep neural network models for practical applications"
    - "non local neural networks"
    - "dilated residual networks"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "training shallow and thin networks for acceleration via knowledge distillation with conditional adversarial networks"
    - "n2n learning network to network compression via policy gradient reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "learning metrics from teachers compact networks for image embedding"):
  with members:
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "metric learning for novelty and anomaly detection"
    - "learning deep representations with probabilistic knowledge transfer"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "rankiqa learning from rankings for no reference image quality assessment"

Connected through hyperedge HE-5 (induced by source paper "training shallow and thin networks for acceleration via knowledge distillation with conditional adversarial networks"):
  with members:
    - "training quantized nets a deeper understanding"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "distral robust multitask reinforcement learning"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "training constrained deconvolutional networks for road scene semantic segmentation"
    - "aggregated residual transformations for deep neural networks"

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)