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: "regularizing rnns by stabilizing activations"
  Abstract: We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance between successive hidden states' norms. #R##N#This penalty term is an effective regularizer for RNNs including LSTMs and IRNNs, improving performance on character-level language modeling and phoneme recognition, and outperforming weight noise and dropout. #R##N#We achieve competitive performance (18.6\% PER) on the TIMIT phoneme recognition task for RNNs evaluated without beam search or an RNN transducer. #R##N#With this penalty term, IRNN can achieve similar performance to LSTM on language modeling, although adding the penalty term to the LSTM results in superior performance. #R##N#Our penalty term also prevents the exponential growth of IRNN's activations outside of their training horizon, allowing them to generalize to much longer sequences.

Connected through hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "kernel and deep regimes in overparametrized models"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"

Connected through hyperedge HE-2 (induced by source paper "rnn based online learning an efficient first order optimization algorithm with a convergence guarantee"):
  with members:
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "on the convergence rate of training recurrent neural networks"
    - "a local regret in nonconvex online learning"
    - "learning with random learning rates"
    - "stable recurrent models"
    - "lstm a search space odyssey"
    - "efficient regret minimization in non convex games"
    - "training deep networks without learning rates through coin betting"

Connected through hyperedge HE-3 (induced by source paper "deep independently recurrent neural network indrnn"):
  with members:
    - "temporal transformer networks joint learning of invariant and discriminative time warping"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"
    - "learning long term dependencies via fourier recurrent units"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"

Connected through hyperedge HE-4 (induced by source paper "text generation with exemplar based adaptive decoding"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "backpropagating through structured argmax using a spigot"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "rational recurrences"
    - "bottom up abstractive summarization"
    - "learning joint semantic parsers from disjoint data"
    - "learning neural templates for text generation"
    - "contextual parameter generation for universal neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "fast es rnn a gpu implementation of the es rnn algorithm"):
  with members:
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "timenet pre trained deep recurrent neural network for time series classification"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-6 (induced by source paper "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"):
  with members:
    - "diagonal rnns in symbolic music modeling"
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "dilated recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "comparative study of cnn and rnn for natural language processing"
    - "skip rnn learning to skip state updates in recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "recent advances in recurrent neural networks"):
  with members:
    - "gated orthogonal recurrent units on learning to forget"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "interpretation of mammogram and chest radiograph reports using deep neural networks preliminary results"
    - "recurrent dropout without memory loss"
    - "recurrent memory network for language modeling"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "independently recurrent neural network indrnn building a longer and deeper rnn"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "lstm a search space odyssey"
    - "pose conditioned spatio temporal attention for human action recognition"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "fast slow recurrent neural networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "joint distance maps based action recognition with convolutional neural networks"

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.NE(Neural and Evolutionary Computing)