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: "on the variance of the adaptive learning rate and beyond"
  Abstract: The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method. All implementations are available at: this https URL.

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "the visual task adaptation benchmark"
    - "using learned optimizers to make models robust to input noise"
    - "deepobs a deep learning optimizer benchmark suite"
    - "nas bench 101 towards reproducible neural architecture search"
    - "on the tunability of optimizers in deep learning"

Connected through hyperedge HE-2 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games"):
  with members:
    - "graph constrained reinforcement learning for natural language action spaces"
    - "does order matter an empirical study on generating multiple keyphrases as a sequence"
    - "nail a general interactive fiction agent"
    - "algorithmic improvements for deep reinforcement learning applied to interactive fiction"
    - "interactive fiction games a colossal adventure"
    - "using local knowledge graph construction to scale seq2seq models to multi document inputs"
    - "learning to speak and act in a fantasy text adventure game"
    - "ledeepchef deep reinforcement learning agent for families of text based games"

Connected through hyperedge HE-3 (induced by source paper "fine grained image classification and retrieval by combining visual and locally pooled textual features"):
  with members:
    - "towards vqa models that can read"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "lookahead optimizer k steps forward 1 step back"
    - "single shot scene text retrieval"
    - "an end to end textspotter with explicit alignment and attention"
    - "learning to navigate for fine grained classification"
    - "e2e mlt an unconstrained end to end method for multi language scene text"

Connected through hyperedge HE-4 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "urban driving with conditional imitation learning"
    - "grip graph based interaction aware trajectory prediction"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multiple futures prediction"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "conditional vehicle trajectories prediction in carla urban environment"
    - "scalability in perception for autonomous driving waymo open dataset"

Connected through hyperedge HE-5 (induced by source paper "meliusnet can binary neural networks achieve mobilenet level accuracy"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "bayesian optimized 1 bit cnns"
    - "searching for accurate binary neural architectures"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "training competitive binary neural networks from scratch"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-6 (induced by source paper "laprop a better way to combine momentum with adaptive gradient"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "optimization for deep learning theory and algorithms"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "roberta a robustly optimized bert pretraining approach"
    - "gradient descent finds global minima of deep neural networks"
    - "a general system of differential equations to model first order adaptive algorithms"

Connected through hyperedge HE-7 (induced by source paper "wide minima density hypothesis and the explore exploit learning rate schedule"):
  with members:
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "towards understanding generalization in gradient based meta learning"
    - "an empirical model of large batch training"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "three factors influencing minima in sgd"

Connected through hyperedge HE-8 (induced by source paper "hierarchical quantized autoencoders"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "rethinking lossy compression the rate distortion perception tradeoff"
    - "the perception distortion tradeoff"
    - "deep generative models for distribution preserving lossy compression"
    - "recent advances in autoencoder based representation learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "variational memory addressing in generative models"

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)