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: "adanet adaptive structural learning of artificial neural networks"
  Abstract: We present new algorithms for adaptively learning artificial neural networks. Our algorithms (AdaNet) adaptively learn both the structure of the network and its weights. They are based on a solid theoretical analysis, including data-dependent generalization guarantees that we prove and discuss in detail. We report the results of large-scale experiments with one of our algorithms on several binary classification tasks extracted from the CIFAR-10 dataset. The results demonstrate that our algorithm can automatically learn network structures with very competitive performance accuracies when compared with those achieved for neural networks found by standard approaches.

Connected through hyperedge HE-1 (induced by source paper "convolutional networks with dense connectivity"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "condensenet an efficient densenet using learned group convolutions"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-2 (induced by source paper "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "automatic model selection for neural networks"
    - "swiftnet using graph propagation as meta knowledge to search highly representative neural architectures"
    - "discovering neural wirings"
    - "pytorch an imperative style high performance deep learning library"
    - "learned step size quantization"
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "continual lifelong learning with neural networks a review"):
  with members:
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "continuous learning in single incremental task scenarios"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "on the role of neurogenesis in overcoming catastrophic forgetting"
    - "a neurorobotic experiment for crossmodal conflict resolution in complex environments"
    - "deep generative dual memory network for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "expectation learning for adaptive crossmodal stimuli association"

Connected through hyperedge HE-4 (induced by source paper "routing networks and the challenges of modular and compositional computation"):
  with members:
    - "a meta mdp approach to exploration for lifelong reinforcement learning"
    - "reinforcement learning without backpropagation or a clock"
    - "the termination critic"
    - "modular networks learning to decompose neural computation"
    - "learning abstract options"
    - "evolutionary architecture search for deep multitask networks"
    - "modular meta learning"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-5 (induced by source paper "autogrow automatic layer growing in deep convolutional networks"):
  with members:
    - "cgap continuous growth and pruning for efficient deep learning"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "evolving deep neural networks"
    - "progressive neural architecture search"
    - "learning efficient convolutional networks through network slimming"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-6 (induced by source paper "tabnet attentive interpretable tabular learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "global explanations of neural networks mapping the landscape of predictions"
    - "deep neural decision trees"
    - "consistent individualized feature attribution for tree ensembles"
    - "compositional attention networks for machine reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive neural trees"
    - "xgboost scalable gpu accelerated learning"

Connected through hyperedge HE-7 (induced by source paper "learning gans and ensembles using discrepancy"):
  with members:
    - "teacher student compression with generative adversarial networks"
    - "efficient gan based anomaly detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "efficient forward architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"

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)