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: "grid long short term memory"
  Abstract: This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images. The network differs from existing deep LSTM architectures in that the cells are connected between network layers as well as along the spatiotemporal dimensions of the data. The network provides a unified way of using LSTM for both deep and sequential computation. We apply the model to algorithmic tasks such as 15-digit integer addition and sequence memorization, where it is able to significantly outperform the standard LSTM. We then give results for two empirical tasks. We find that 2D Grid LSTM achieves 1.47 bits per character on the Wikipedia character prediction benchmark, which is state-of-the-art among neural approaches. In addition, we use the Grid LSTM to define a novel two-dimensional translation model, the Reencoder, and show that it outperforms a phrase-based reference system on a Chinese-to-English translation task.

Connected through hyperedge HE-1 (induced by source paper "arma nets expanding receptive field for dense prediction"):
  with members:
    - "scale aware trident networks for object detection"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deformable convnets v2 more deformable better results"
    - "smoothed dilated convolutions for improved dense prediction"
    - "contextvp fully context aware video prediction"
    - "active convolution learning the shape of convolution for image classification"
    - "learning affinity via spatial propagation networks"
    - "deformable convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "neural machine translation with joint representation"):
  with members:
    - "learning deep transformer models for machine translation"
    - "towards two dimensional sequence to sequence model in neural machine translation"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "understanding deep learning requires rethinking generalization"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "neural arithmetic units"):
  with members:
    - "learning dexterous in hand manipulation"
    - "neural arithmetic expression calculator"
    - "neural arithmetic logic units"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on evaluating the generalization of lstm models in formal languages"
    - "improving the neural gpu architecture for algorithm learning"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "adam a method for stochastic optimization"

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"
    - "neural programmer interpreters"
    - "adam a method for stochastic optimization"
    - "learning to execute"

Connected through hyperedge HE-5 (induced by source paper "neural shuffle exchange networks sequence processing in o n log n time"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "tensor2tensor for neural machine translation"
    - "recent advances in neural program synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-6 (induced by source paper "stabilizing transformers for reinforcement learning"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "meta reinforcement learning as task inference"
    - "transformer xl attentive language models beyond a fixed length context"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "generating wikipedia by summarizing long sequences"

Connected through hyperedge HE-7 (induced by source paper "cubic lstms for video prediction"):
  with members:
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"
    - "motion prediction under multimodality with conditional stochastic networks"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "recurrent environment simulators"
    - "mocogan decomposing motion and content for video generation"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "understanding lstm a tutorial into long short term memory recurrent neural networks"):
  with members:
    - "listen attend and spell"
    - "attention based models for speech recognition"
    - "a neural conversational model"
    - "neural turing machines"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "a clockwork rnn"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

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)