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: "bayesnas a bayesian approach for neural architecture search"
  Abstract: One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training. It can be treated as a Network Compression problem on the architecture parameters from an over-parameterized network. However, there are two issues associated with most one-shot NAS methods. First, dependencies between a node and its predecessors and successors are often disregarded which result in improper treatment over zero operations. Second, architecture parameters pruning based on their magnitude is questionable. In this paper, we employ the classic Bayesian learning approach to alleviate these two issues by modeling architecture parameters using hierarchical automatic relevance determination (HARD) priors. Unlike other NAS methods, we train the over-parameterized network for only one epoch then update the architecture. Impressively, this enabled us to find the architecture on CIFAR-10 within only 0.2 GPU days using a single GPU. Competitive performance can be also achieved by transferring to ImageNet. As a byproduct, our approach can be applied directly to compress convolutional neural networks by enforcing structural sparsity which achieves extremely sparse networks without accuracy deterioration.

Connected through hyperedge HE-1 (induced by source paper "stabilizing differentiable architecture search via perturbation based regularization"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "understanding architectures learnt by cell based neural architecture search"
    - "efficient neural architecture search via proximal iterations"
    - "certified adversarial robustness via randomized smoothing"
    - "searching for a robust neural architecture in four gpu hours"
    - "fair darts eliminating unfair advantages in differentiable architecture search"
    - "understanding and robustifying differentiable architecture search"
    - "darts improved differentiable architecture search with early stopping"

Connected through hyperedge HE-2 (induced by source paper "how to train your super net an analysis of training heuristics in weight sharing nas"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "nas bench 201 extending the scope of reproducible neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "on network design spaces for visual recognition"
    - "nas evaluation is frustratingly hard"
    - "searching for a robust neural architecture in four gpu hours"
    - "detnas backbone search for object detection"
    - "understanding and robustifying differentiable architecture search"

Connected through hyperedge HE-3 (induced by source paper "latency aware differentiable neural architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-4 (induced by source paper "semi supervised neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "neural predictor for neural architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "fastspeech fast robust and controllable text to speech"
    - "the evolved transformer"
    - "single path one shot neural architecture search with uniform sampling"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-5 (induced by source paper "efficient sample based neural architecture search with learnable predictor"):
  with members:
    - "nas bench 102 extending the scope of reproducible neural architecture search"
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree search"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-6 (induced by source paper "the effect of data ordering in image classification"):
  with members:
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "scalable nas with factorizable architectural parameters"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "squeezenas fast neural architecture search for faster semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "sgas sequential greedy architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "kpconv flexible and deformable convolution for point clouds"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "evaluating the search phase of neural architecture search"
    - "fast graph representation learning with pytorch geometric"
    - "deepgcns can gcns go as deep as cnns"
    - "deepgcns making gcns go as deep as cnns"
    - "regularized evolution for image classifier architecture search"

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)