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: "beyond convexity stochastic quasi convex optimization"
  Abstract: Stochastic convex optimization is a basic and well studied primitive in machine learning. It is well known that convex and Lipschitz functions can be minimized efficiently using Stochastic Gradient Descent (SGD). The Normalized Gradient Descent (NGD) algorithm, is an adaptation of Gradient Descent, which updates according to the direction of the gradients, rather than the gradients themselves. In this paper we analyze a stochastic version of NGD and prove its convergence to a global minimum for a wider class of functions: we require the functions to be quasi-convex and locally-Lipschitz. Quasi-convexity broadens the con- cept of unimodality to multidimensions and allows for certain types of saddle points, which are a known hurdle for first-order optimization methods such as gradient descent. Locally-Lipschitz functions are only required to be Lipschitz in a small region around the optimum. This assumption circumvents gradient explosion, which is another known hurdle for gradient descent variants. Interestingly, unlike the vanilla SGD algorithm, the stochastic normalized gradient descent algorithm provably requires a minimal minibatch size.

Connected through hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "momentum improves normalized sgd"):
  with members:
    - "reducing the variance in online optimization by transporting past gradients"
    - "memory efficient adaptive optimization"
    - "momentum based variance reduction in non convex sgd"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "on the convergence of adam and beyond"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adafactor adaptive learning rates with sublinear memory cost"

Connected through hyperedge HE-3 (induced by source paper "mix match training convnets with mixed image sizes for improved accuracy speed and scale resiliency"):
  with members:
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "faster neural network training with data echoing"
    - "fixing the train test resolution discrepancy"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "multigrain a unified image embedding for classes and instances"
    - "yolov3 an incremental improvement"
    - "regularized evolution for image classifier architecture search"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-4 (induced by source paper "stochastic gradient methods with layer wise adaptive moments for training of deep networks"):
  with members:
    - "towards a human like open domain chatbot"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "transformer xl attentive language models beyond a fixed length context"
    - "on the convergence of adam and beyond"
    - "block normalized gradient method an empirical study for training deep neural network"
    - "bag of tricks for image classification with convolutional neural networks"
    - "normalized direction preserving adam"
    - "scaling neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "trajectory normalized gradients for distributed optimization"):
  with members:
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "an alternative view when does sgd escape local minima"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "self normalizing neural networks"
    - "gradient sparsification for communication efficient distributed optimization"
    - "sparse communication for distributed gradient descent"

Connected through hyperedge HE-6 (induced by source paper "dante deep alternations for training neural networks"):
  with members:
    - "learning relu networks via alternating minimization"
    - "a provably correct algorithm for deep learning that actually works"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "recovery guarantees for one hidden layer neural networks"
    - "failures of gradient based deep learning"
    - "decoupled neural interfaces using synthetic gradients"
    - "training neural networks without gradients a scalable admm approach"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"

Connected through hyperedge HE-7 (induced by source paper "a class of parameterized loss functions for classification optimization tradeoffs and robustness characteristics"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"
    - "a tunable loss function for binary classification"
    - "a tunable measure for information leakage"
    - "rademacher complexity for adversarially robust generalization"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "context aware generative adversarial privacy"

Connected through hyperedge HE-8 (induced by source paper "the geometry of sign gradient descent"):
  with members:
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "ellipsoidal trust region methods and the marginal value of hessian information for neural network training"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "block normalized gradient method an empirical study for training deep neural network"
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"

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)