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: "a clockwork rnn"
  Abstract: Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs. Recurrent Neural Networks (RNNs) have the ability, in theory, to cope with these temporal dependencies by virtue of the short-term memory implemented by their recurrent (feedback) connections. However, in practice they are difficult to train successfully when the long-term memory is required. This paper introduces a simple, yet powerful modification to the standard RNN architecture, the Clockwork RNN (CW-RNN), in which the hidden layer is partitioned into separate modules, each processing inputs at its own temporal granularity, making computations only at its prescribed clock rate. Rather than making the standard RNN models more complex, CW-RNN reduces the number of RNN parameters, improves the performance significantly in the tasks tested, and speeds up the network evaluation. The network is demonstrated in preliminary experiments involving two tasks: audio signal generation and TIMIT spoken word classification, where it outperforms both RNN and LSTM networks.

Connected through hyperedge HE-1 (induced by source paper "transformer xl attentive language models beyond a fixed length context"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mesh tensorflow deep learning for supercomputers"

Connected through hyperedge HE-2 (induced by source paper "recurrent independent mechanisms"):
  with members:
    - "learning powerful policies by using consistent dynamics model"
    - "routing networks and the challenges of modular and compositional computation"
    - "reinforcement learning with competitive ensembles of information constrained primitives"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "modular networks learning to decompose neural computation"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"

Connected through hyperedge HE-3 (induced by source paper "improving the gating mechanism of recurrent neural networks"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "transformer xl attentive language models beyond a fixed length context"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "towards binary valued gates for robust lstm training"
    - "optimizing agent behavior over long time scales by transporting value"
    - "trellis networks for sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "learning implicitly recurrent cnns through parameter sharing"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "regularized evolution for image classifier architecture search"
    - "neural arithmetic logic units"
    - "snas stochastic neural architecture search"
    - "sparsely aggregated convolutional networks"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "making neural programming architectures generalize via recursion"

Connected through hyperedge HE-5 (induced by source paper "exploring interpretable lstm neural networks over multi variable data"):
  with members:
    - "fine grained attention mechanism for neural machine translation"
    - "an interpretable lstm neural network for autoregressive exogenous model"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "deep multi output forecasting learning to accurately predict blood glucose trajectories"
    - "methods for interpreting and understanding deep neural networks"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "beyond word importance contextual decomposition to extract interactions from lstms"

Connected through hyperedge HE-6 (induced by source paper "lifelong sequential modeling with personalized memorization for user response prediction"):
  with members:
    - "on training recurrent neural networks for lifelong learning"
    - "collaborative memory network for recommendation systems"
    - "learning multi touch conversion attribution with dual attention mechanisms for online advertising"
    - "next item recommendation with self attention"
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "recurrent neural networks for long and short term sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"

Connected through hyperedge HE-7 (induced by source paper "human level performance in 3d multiplayer games with population based reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning with opponent learning awareness"
    - "feudal networks for hierarchical reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "emergence of grounded compositional language in multi agent populations"
    - "population based training of neural networks"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-8 (induced by source paper "better long range dependency by bootstrapping a mutual information regularizer"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "mine mutual information neural estimation"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "topic compositional neural language model"
    - "attention is all you need"
    - "adversarially regularized autoencoders"

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)