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: "darkrank accelerating deep metric learning via cross sample similarities transfer"
  Abstract: We have witnessed rapid evolution of deep neural network architecture design in the past years. These latest progresses greatly facilitate the developments in various areas such as computer vision, natural language processing, etc. However, along with the extraordinary performance, these state-of-the-art models also bring in expensive computational cost. Directly deploying these models into applications with real-time requirement is still infeasible. Recently, Hinton etal. have shown that the dark knowledge within a powerful teacher model can significantly help the training of a smaller and faster student network. These knowledge are vastly beneficial to improve the generalization ability of the student model. Inspired by their work, we introduce a new type of knowledge -- cross sample similarities for model compression and acceleration. This knowledge can be naturally derived from deep metric learning model. To transfer them, we bring the learning to rank technique into deep metric learning formulation. We test our proposed DarkRank on the pedestrian re-identification task. The results are quite encouraging. Our DarkRank can improve over the baseline method by a large margin. Moreover, it is fully compatible with other existing methods. When combined, the performance can be further boosted.

Connected through hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "design automation for efficient deep learning computing"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "semantic image synthesis with spatially adaptive normalization"
    - "co evolutionary compression for unpaired image translation"
    - "compressing gans using knowledge distillation"
    - "video to video synthesis"

Connected through hyperedge HE-2 (induced by source paper "efficient semantic video segmentation with per frame inference"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "deep video inpainting"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dynamic video segmentation network"

Connected through hyperedge HE-3 (induced by source paper "understanding and improving knowledge distillation"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "when does label smoothing help"
    - "revisit knowledge distillation a teacher free framework"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "bag of tricks for image classification with convolutional neural networks"
    - "knowledge transfer with jacobian matching"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "large scale distributed neural network training through online distillation"

Connected through hyperedge HE-4 (induced by source paper "depara deep attribution graph for deep knowledge transferability"):
  with members:
    - "relational knowledge distillation"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "deep model transferability from attribution maps"
    - "similarity preserving knowledge distillation"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-5 (induced by source paper "interpretation and simplification of deep forest"):
  with members:
    - "searching for mobilenetv3"
    - "explainable machine learning for scientific insights and discoveries"
    - "multi layered gradient boosting decision trees"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning a critical appraisal"
    - "explainable and explicit visual reasoning over scene graphs"

Connected through hyperedge HE-6 (induced by source paper "structured knowledge distillation for dense prediction"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ocnet object context network for scene parsing"

Connected through hyperedge HE-7 (induced by source paper "highlight every step knowledge distillation via collaborative teaching"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "model compression via distillation and quantization"
    - "private model compression via knowledge distillation"
    - "amalgamating knowledge towards comprehensive classification"
    - "improving fast segmentation with teacher student learning"
    - "label refinery improving imagenet classification through label progression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-8 (induced by source paper "adversarial based knowledge distillation for multi model ensemble and noisy data refinement"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "relational knowledge distillation"
    - "towards instance level image to image translation"
    - "snapshot distillation teacher student optimization in one generation"
    - "knowledge distillation by on the fly native ensemble"
    - "multimodal unsupervised image to image translation"
    - "large scale distributed neural network training through online distillation"
    - "meal multi model ensemble via adversarial learning"

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)