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: "fully learnable group convolution for acceleration of deep neural networks"
  Abstract: Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e.g. smartphone, embedded devices, etc. To reduce the high computational and memory cost, in this work, we propose a fully learnable group convolution module (FLGC for short) which is quite efficient and can be embedded into any deep neural networks for acceleration. Specifically, our proposed method automatically learns the group structure in the training stage in a fully end-to-end manner, leading to a better structure than the existing pre-defined, two-steps, or iterative strategies. Moreover, our method can be further combined with depthwise separable convolution, resulting in 5 times acceleration than the vanilla Resnet50 on single CPU. An additional advantage is that in our FLGC the number of groups can be set as any value, but not necessarily 2^k as in most existing methods, meaning better tradeoff between accuracy and speed. As evaluated in our experiments, our method achieves better performance than existing learnable group convolution and standard group convolution when using the same number of groups.

Connected through hyperedge HE-1 (induced by source paper "model agnostic structured sparsification with learnable channel shuffle"):
  with members:
    - "search to distill pearls are everywhere but not the eyes"
    - "differentiable learning to group channels via groupable convolutional neural networks"
    - "importance estimation for neural network pruning"
    - "pytorch an imperative style high performance deep learning library"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "differentiable learning to group channels via groupable convolutional neural networks"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-3 (induced by source paper "tuning algorithms and generators for efficient edge inference"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "learning grouped convolution for efficient domain adaptation"
    - "mpdcompress matrix permutation decomposition algorithm for deep neural network compression"
    - "chamnet towards efficient network design through platform aware model adaptation"
    - "block sparse recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "efficient structured pruning and architecture searching for group convolution"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "building efficient deep neural networks with unitary group convolutions"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "extreme network compression via filter group approximation"
    - "mnasnet platform aware neural architecture search for mobile"
    - "rethinking the value of network pruning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-5 (induced by source paper "learning grouped convolution for efficient domain adaptation"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "building efficient deep neural networks with unitary group convolutions"
    - "darts differentiable architecture search"
    - "extreme network compression via filter group approximation"
    - "mnasnet platform aware neural architecture search for mobile"
    - "rethinking the value of network pruning"
    - "nisp pruning networks using neuron importance score propagation"
    - "domain adaptive deep network compression"

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)