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: "learning to execute"
  Abstract: Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train. Our interest lies in empirically evaluating the expressiveness and the learnability of LSTMs in the sequence-to-sequence regime by training them to evaluate short computer programs, a domain that has traditionally been seen as too complex for neural networks. We consider a simple class of programs that can be evaluated with a single left-to-right pass using constant memory. Our main result is that LSTMs can learn to map the character-level representations of such programs to their correct outputs. Notably, it was necessary to use curriculum learning, and while conventional curriculum learning proved ineffective, we developed a new variant of curriculum learning that improved our networks' performance in all experimental conditions. The improved curriculum had a dramatic impact on an addition problem, making it possible to train an LSTM to add two 9-digit numbers with 99% accuracy.

Connected through hyperedge HE-1 (induced by source paper "curriculum by texture"):
  with members:
    - "do better imagenet models transfer better"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "simple baselines for human pose estimation and tracking"
    - "automated curriculum learning for neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "cycada cycle consistent adversarial domain adaptation"
    - "teacher student curriculum learning"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "facilitating sql query composition and analysis"):
  with members:
    - "query2vec nlp meets databases for generalized workload analytics"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a survey of machine learning for big code and naturalness"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "convolutional neural networks for text categorization shallow word level vs deep character level"
    - "very deep convolutional networks for text classification"
    - "database meets deep learning challenges and opportunities"

Connected through hyperedge HE-3 (induced by source paper "progress extrapolating algorithmic learning to arbitrary sequence lengths"):
  with members:
    - "universal transformers"
    - "learning efficient algorithms with hierarchical attentive memory"
    - "improving the neural gpu architecture for algorithm learning"
    - "making neural programming architectures generalize via recursion"
    - "adaptive computation time for recurrent neural networks"
    - "neural random access machines"
    - "neural programmer interpreters"
    - "neural gpus learn algorithms"

Connected through hyperedge HE-4 (induced by source paper "inalu improved neural arithmetic logic unit"):
  with members:
    - "flow based network traffic generation using generative adversarial networks"
    - "measuring arithmetic extrapolation performance"
    - "neural arithmetic expression calculator"
    - "neural arithmetic logic units"
    - "improving the neural gpu architecture for algorithm learning"
    - "grid long short term memory"
    - "neural programmer interpreters"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "progressive growing of neural odes"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "interpolation prediction networks for irregularly sampled time series"
    - "neural ordinary differential equations"
    - "teacher student curriculum learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"

Connected through hyperedge HE-6 (induced by source paper "towards practical multi object manipulation using relational reinforcement learning"):
  with members:
    - "learning to control self assembling morphologies a study of generalization via modularity"
    - "on first order meta learning algorithms"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning dexterous in hand manipulation"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "modulated policy hierarchies"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "a study on overfitting in deep reinforcement learning"

Connected through hyperedge HE-7 (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-8 (induced by source paper "state regularized recurrent neural networks"):
  with members:
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "automatic rule extraction from long short term memory 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)