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: "multiplicative lstm for sequence modelling"
  Abstract: We introduce multiplicative LSTM (mLSTM), a recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition functions for each possible input, which we argue makes it more expressive for autoregressive density estimation. We demonstrate empirically that mLSTM outperforms standard LSTM and its deep variants for a range of character level language modelling tasks. In this version of the paper, we regularise mLSTM to achieve 1.27 bits/char on text8 and 1.24 bits/char on Hutter Prize. We also apply a purely byte-level mLSTM on the WikiText-2 dataset to achieve a character level entropy of 1.26 bits/char, corresponding to a word level perplexity of 88.8, which is comparable to word level LSTMs regularised in similar ways on the same task.

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 "explicit sparse transformer concentrated attention through explicit selection"):
  with members:
    - "adaptively sparse transformers"
    - "muse parallel multi scale attention for sequence to sequence learning"
    - "pay less attention with lightweight and dynamic convolutions"
    - "sparse sequence to sequence models"
    - "joint source target self attention with locality constraints"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "universal transformers"

Connected through hyperedge HE-3 (induced by source paper "compressive transformers for long range sequence modelling"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "the curious case of neural text degeneration"
    - "dynamic evaluation of transformer language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "large memory layers with product keys"

Connected through hyperedge HE-4 (induced by source paper "character level recurrent neural networks in practice comparing training and sampling schemes"):
  with members:
    - "large scale user modeling with recurrent neural networks for music discovery on multiple time scales"
    - "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"
    - "fast slow recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "improved recurrent neural networks for session based recommendations"
    - "tying word vectors and word classifiers a loss framework for language modeling"

Connected through hyperedge HE-5 (induced by source paper "encoding word order in complex embeddings"):
  with members:
    - "a tensorized transformer for language modeling"
    - "inter sentence relation extraction with document level graph convolutional neural network"
    - "transformer xl attentive language models beyond a fixed length context"
    - "semantic hilbert space for text representation learning"
    - "cnm an interpretable complex valued network for matching"
    - "self attention with relative position representations"
    - "quantum inspired complex word embedding"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "multi zone unit for recurrent neural networks"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "a novel aspect guided deep transition model for aspect based sentiment analysis"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "recurrent attention unit"
    - "long short term attention"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "aspect based sentiment analysis with gated convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "a neural virtual anchor synthesizer based on seq2seq and gan models"):
  with members:
    - "video synthesis of human upper body with realistic face"
    - "recycle gan unsupervised video retargeting"
    - "deep contextualized word representations"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a style based generator architecture for generative adversarial networks"
    - "everybody dance now"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "compressing gradient optimizers via count sketches"):
  with members:
    - "large scale language modeling converging on 40gb of text in four hours"
    - "extreme classification in log memory"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "hash embeddings for efficient word representations"
    - "breaking the softmax bottleneck a high rank rnn language model"

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)