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: "irlas inverse reinforcement learning for architecture search"
  Abstract: In this paper, we propose an inverse reinforcement learning method for architecture search (IRLAS), which trains an agent to learn to search network structures that are topologically inspired by human-designed network. Most existing architecture search approaches totally neglect the topological characteristics of architectures, which results in complicated architecture with a high inference latency. Motivated by the fact that human-designed networks are elegant in topology with a fast inference speed, we propose a mirror stimuli function inspired by biological cognition theory to extract the abstract topological knowledge of an expert human-design network (ResNeXt). To avoid raising a too strong prior over the search space, we introduce inverse reinforcement learning to train the mirror stimuli function and exploit it as a heuristic guidance for architecture search, easily generalized to different architecture search algorithms. On CIFAR-10, the best architecture searched by our proposed IRLAS achieves 2.60% error rate. For ImageNet mobile setting, our model achieves a state-of-the-art top-1 accuracy 75.28%, while being 2~4x faster than most auto-generated architectures. A fast version of this model achieves 10% faster than MobileNetV2, while maintaining a higher accuracy.

Connected through hyperedge HE-1 (induced by source paper "intra ensemble in neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "incremental training of deep convolutional neural networks"
    - "efficient progressive neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "graph guided architecture search for real time semantic segmentation"):
  with members:
    - "customizable architecture search for semantic segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "reinforced evolutionary neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-3 (induced by source paper "am lfs automl for loss function search"):
  with members:
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "learning to teach with dynamic loss functions"
    - "harmonious attention network for person re identification"
    - "cosface large margin cosine loss for deep face recognition"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-4 (induced by source paper "scalable reinforcement learning based neural architecture search for cancer deep learning research"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "evolutionary neural automl for deep learning"
    - "evaluating the search phase of neural architecture search"
    - "balsam automated scheduling and execution of dynamic data intensive hpc workflows"
    - "evolutionary neural architecture search for image restoration"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"

Connected through hyperedge HE-5 (induced by source paper "when nas meets robustness in search of robust architectures against adversarial attacks"):
  with members:
    - "deeper insights into weight sharing in neural architecture search"
    - "intriguing properties of adversarial training"
    - "exploring randomly wired neural networks for image recognition"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "feature denoising for improving adversarial robustness"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-6 (induced by source paper "betanas balanced training and selective drop for neural architecture search"):
  with members:
    - "hm nas efficient neural architecture search via hierarchical masking"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "searching for mobilenetv3"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "evaluating the search phase of neural architecture search"
    - "darts improved differentiable architecture search with early stopping"

Connected through hyperedge HE-7 (induced by source paper "online hyper parameter learning for auto augmentation strategy"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "autoaugment learning augmentation policies from data"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "progressive neural architecture search"

Connected through hyperedge HE-8 (induced by source paper "efficient novelty driven neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "single path one shot neural architecture search with uniform sampling"
    - "bayesnas a bayesian approach for neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "a study of the learning progress in neural architecture search techniques"
    - "graph hypernetworks for neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"

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)