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 learning dynamics of deep neural networks"
  Abstract: While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely misunderstood. In this work, we study the case of binary classification and prove various properties of learning in such networks under strong assumptions such as linear separability of the data. Extending existing results from the linear case, we confirm empirical observations by proving that the classification error also follows a sigmoidal shape in nonlinear architectures. We show that given proper initialization, learning expounds parallel independent modes and that certain regions of parameter space might lead to failed training. We also demonstrate that input norm and features' frequency in the dataset lead to distinct convergence speeds which might shed some light on the generalization capabilities of deep neural networks. We provide a comparison between the dynamics of learning with cross-entropy and hinge losses, which could prove useful to understand recent progress in the training of generative adversarial networks. Finally, we identify a phenomenon that we baptize \textit{gradient starvation} where the most frequent features in a dataset prevent the learning of other less frequent but equally informative features.

Connected through hyperedge HE-1 (induced by source paper "unsupervised state representation learning in atari"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "on variational bounds of mutual information"
    - "multi object representation learning with iterative variational inference"
    - "wasserstein dependency measure for representation learning"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"
    - "object oriented dynamics predictor"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"

Connected through hyperedge HE-2 (induced by source paper "deep likelihood network for image restoration with multiple degradations"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "the perception distortion tradeoff"
    - "physics based generative adversarial models for image restoration and beyond"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "deep image prior"
    - "learning a single convolutional super resolution network for multiple degradations"
    - "convolutional dictionary learning via local processing"

Connected through hyperedge HE-3 (induced by source paper "the implicit bias of depth how incremental learning drives generalization"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "sgd on neural networks learns functions of increasing complexity"
    - "kernel and deep regimes in overparametrized models"
    - "gradient dynamics of shallow univariate relu networks"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "implicit regularization of discrete gradient dynamics in linear neural networks"):
  with members:
    - "implicit bias of gradient descent on linear convolutional networks"
    - "step size matters in deep learning"
    - "a mathematical theory of semantic development in deep neural networks"
    - "implicit regularization in deep learning"
    - "geometry of optimization and implicit regularization in deep learning"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"
    - "understanding deep learning requires rethinking generalization"
    - "path sgd path normalized optimization in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "an empirical study of example forgetting during deep neural network learning"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "identifying generalization properties in neural networks"
    - "not all samples are created equal deep learning with importance sampling"
    - "an alternative view when does sgd escape local minima"
    - "learning what data to learn"
    - "improved regularization of convolutional neural networks with cutout"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-6 (induced by source paper "understanding learning dynamics of language models with svcca"):
  with members:
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep contextualized word representations"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "deep rnns encode soft hierarchical syntax"
    - "sharp nearby fuzzy far away how neural language models use context"

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)