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: "knowledge distillation in generations more tolerant teachers educate better students"
  Abstract: We focus on the problem of training a deep neural network in generations. The flowchart is that, in order to optimize the target network (student), another network (teacher) with the same architecture is first trained, and used to provide part of supervision signals in the next stage. While this strategy leads to a higher accuracy, many aspects (e.g., why teacher-student optimization helps) still need further explorations. #R##N#This paper studies this problem from a perspective of controlling the strictness in training the teacher network. Existing approaches mostly used a hard distribution (e.g., one-hot vectors) in training, leading to a strict teacher which itself has a high accuracy, but we argue that the teacher needs to be more tolerant, although this often implies a lower accuracy. The implementation is very easy, with merely an extra loss term added to the teacher network, facilitating a few secondary classes to emerge and complement to the primary class. Consequently, the teacher provides a milder supervision signal (a less peaked distribution), and makes it possible for the student to learn from inter-class similarity and potentially lower the risk of over-fitting. Experiments are performed on standard image classification tasks (CIFAR100 and ILSVRC2012). Although the teacher network behaves less powerful, the students show a persistent ability growth and eventually achieve higher classification accuracies than other competitors. Model ensemble and transfer feature extraction also verify the effectiveness of our approach.

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 "multilingual neural machine translation with knowledge distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "sentence wise smooth regularization for sequence to sequence learning"
    - "universal neural machine translation for extremely low resource languages"
    - "meta learning for low resource neural machine translation"
    - "large scale distributed neural network training through online distillation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "double path networks for sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "rethinking normalization and elimination singularity in neural networks"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "a mean field theory of batch normalization"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "weight standardization"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "multi scale spatially asymmetric recalibration for image classification"
    - "differentiable learning to normalize via switchable normalization"
    - "deep co training for semi supervised image recognition"

Connected through hyperedge HE-4 (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"
    - "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"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "copying machine learning classifiers"):
  with members:
    - "teacher student compression with generative adversarial networks"
    - "sampling unknown decision functions to build classifier copies"
    - "credit risk prediction in an imbalanced social lending environment"
    - "towards global explanations for credit risk scoring"
    - "self normalizing neural networks"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "machine learning models that remember too much"
    - "beyond the technical challenges for deploying machine learning solutions in a software company"

Connected through hyperedge HE-6 (induced by source paper "deepmimic mentor student unlabeled data based training"):
  with members:
    - "stealing knowledge from protected deep neural networks using composite unlabeled data"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "robust student network learning"
    - "learning transferable architectures for scalable image recognition"
    - "low shot learning with imprinted weights"
    - "mixup beyond empirical risk minimization"
    - "squeeze and excitation networks"
    - "softtarget regularization an effective technique to reduce over fitting in neural networks"

Connected through hyperedge HE-7 (induced by source paper "token level ensemble distillation for grapheme to phoneme conversion"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "the evolved transformer"
    - "universal transformers"
    - "achieving human parity on automatic chinese to english news translation"
    - "attending to mathematical language with transformers"
    - "sentence wise smooth regularization for sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "quest quantized embedding space for transferring knowledge"):
  with members:
    - "on the efficacy of knowledge distillation"
    - "relational knowledge distillation"
    - "variational information distillation for knowledge transfer"
    - "contrastive representation distillation"
    - "revisit knowledge distillation a teacher free framework"
    - "similarity preserving knowledge distillation"
    - "paraphrasing complex network network compression via factor transfer"
    - "learning deep representations with probabilistic knowledge transfer"

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)