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: "equilibrated adaptive learning rates for non convex optimization"
  Abstract: Parameter-specific adaptive learning rate methods are computationally efficient ways to reduce the ill-conditioning problems encountered when training large deep networks. Following recent work that strongly suggests that most of the critical points encountered when training such networks are saddle points, we find how considering the presence of negative eigenvalues of the Hessian could help us design better suited adaptive learning rate schemes. We show that the popular Jacobi preconditioner has undesirable behavior in the presence of both positive and negative curvature, and present theoretical and empirical evidence that the so-called equilibration preconditioner is comparatively better suited to non-convex problems. We introduce a novel adaptive learning rate scheme, called ESGD, based on the equilibration preconditioner. Our experiments show that ESGD performs as well or better than RMSProp in terms of convergence speed, always clearly improving over plain stochastic gradient descent.

Connected through hyperedge HE-1 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "normalized direction preserving adam"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "analyzing and improving the image quality of stylegan"):
  with members:
    - "style generator inversion for image enhancement and animation"
    - "source generator attribution via inversion"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "msg gan multi scale gradient gan for stable image synthesis"
    - "interpreting the latent space of gans for semantic face editing"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "attributing fake images to gans analyzing fingerprints in generated images"

Connected through hyperedge HE-3 (induced by source paper "interpretable neural networks for predicting mortality risk using multi modal electronic health records"):
  with members:
    - "a deep neural network to enhance prediction of 1 year mortality using echocardiographic videos of the heart"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "a survey on deep learning in medical image analysis"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-4 (induced by source paper "a taxonomy of channel pruning signals in cnns"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "noiseout a simple way to prune neural networks"
    - "pruning convolutional neural networks for resource efficient inference"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"
    - "structured pruning of deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "neural chinese named entity recognition via cnn lstm crf and joint training with word segmentation"):
  with members:
    - "neural chinese word segmentation with dictionary knowledge"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multi task domain adaptation for sequence tagging"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "boosting named entity recognition with neural character embeddings"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "neural chinese word segmentation with lexicon and unlabeled data via posterior regularization"):
  with members:
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "neural chinese word segmentation with dictionary knowledge"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation with rich pretraining"
    - "transfer deep learning for low resource chinese word segmentation with a novel neural network"
    - "multi task domain adaptation for sequence tagging"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "loss aware weight quantization of deep networks"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "training quantized nets a deeper understanding"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "ternary neural networks with fine grained quantization"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ternary weight networks"

Connected through hyperedge HE-8 (induced by source paper "a convolutional feature map based deep network targeted towards traffic detection and classification"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "exploiting depth from single monocular images for object detection and semantic segmentation"
    - "citypersons a diverse dataset for pedestrian detection"
    - "adaptive learning rate via covariance matrix based preconditioning for deep neural networks"
    - "noscope optimizing neural network queries over video at scale"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "attentive contexts for object detection"

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)