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: "darc differentiable architecture compression"
  Abstract: In many learning situations, resources at inference time are significantly more constrained than resources at training time. This paper studies a general paradigm, called Differentiable ARchitecture Compression (DARC), that combines model compression and architecture search to learn models that are resource-efficient at inference time. Given a resource-intensive base architecture, DARC utilizes the training data to learn which sub-components can be replaced by cheaper alternatives. The high-level technique can be applied to any neural architecture, and we report experiments on state-of-the-art convolutional neural networks for image classification. For a WideResNet with $97.2\%$ accuracy on CIFAR-10, we improve single-sample inference speed by $2.28\times$ and memory footprint by $5.64\times$, with no accuracy loss. For a ResNet with $79.15\%$ Top1 accuracy on ImageNet, we improve batch inference speed by $1.29\times$ and memory footprint by $3.57\times$ with $1\%$ accuracy loss. We also give theoretical Rademacher complexity bounds in simplified cases, showing how DARC avoids overfitting despite over-parameterization.

Connected through hyperedge HE-1 (induced by source paper "class dependent compression of deep neural networks"):
  with members:
    - "knowledge squeezed adversarial network compression"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "exploring randomly wired neural networks for image recognition"
    - "discovering neural wirings"
    - "class balanced loss based on effective number of samples"
    - "compressing gans using knowledge distillation"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "automatic design of cnns via differentiable neural architecture search for polsar image classification"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "efficiently utilizing complex valued polsar image data via a multi task deep learning framework"
    - "polarimetric convolutional network for polsar image classification"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "rudder return decomposition for delayed rewards"

Connected through hyperedge HE-3 (induced by source paper "optimized cnn for polsar image classification via differentiable neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "efficiently utilizing complex valued polsar image data via a multi task deep learning framework"
    - "evolutionary architecture search for deep multitask networks"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "rudder return decomposition for delayed rewards"
    - "learning transferable architectures for scalable image recognition"
    - "deep complex 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.LG(Machine Learning)