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: "self referenced deep learning"
  Abstract: Knowledge distillation is an effective approach to transferring knowledge from a teacher neural network to a student target network for satisfying the low-memory and fast running requirements in practice use. Whilst being able to create stronger target networks compared to the vanilla non-teacher based learning strategy, this scheme needs to train additionally a large teacher model with expensive computational cost. In this work, we present a Self-Referenced Deep Learning (SRDL) strategy. Unlike both vanilla optimisation and existing knowledge distillation, SRDL distils the knowledge discovered by the in-training target model back to itself to regularise the subsequent learning procedure therefore eliminating the need for training a large teacher model. SRDL improves the model generalisation performance compared to vanilla learning and conventional knowledge distillation approaches with negligible extra computational cost. Extensive evaluations show that a variety of deep networks benefit from SRDL resulting in enhanced deployment performance on both coarse-grained object categorisation tasks (CIFAR10, CIFAR100, Tiny ImageNet, and ImageNet) and fine-grained person instance identification tasks (Market-1501).

Connected through hyperedge HE-1 (induced by source paper "darts improved differentiable architecture search with early stopping"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "efficient neural architecture search via proximal iterations"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "exploring randomly wired neural networks for image recognition"
    - "neural input search for large scale recommendation models"
    - "xnas neural architecture search with expert advice"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-2 (induced by source paper "an embarrassingly simple approach for knowledge distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "self supervised knowledge distillation using singular value decomposition"
    - "large scale distributed neural network training through online distillation"
    - "adversarial network compression"
    - "progressive neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "focal loss for dense object detection"
    - "deep mutual learning"

Connected through hyperedge HE-3 (induced by source paper "fast human pose estimation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "person search by multi scale matching"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "large scale distributed neural network training through online distillation"
    - "deep mutual learning"
    - "learning feature pyramids for human pose estimation"
    - "knowledge guided deep fractal neural networks for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

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)