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 longer memory in recurrent neural networks"
  Abstract: Recurrent neural network is a powerful model that learns temporal patterns in sequential data. For a long time, it was believed that recurrent networks are difficult to train using simple optimizers, such as stochastic gradient descent, due to the so-called vanishing gradient problem. In this paper, we show that learning longer term patterns in real data, such as in natural language, is perfectly possible using gradient descent. This is achieved by using a slight structural modification of the simple recurrent neural network architecture. We encourage some of the hidden units to change their state slowly by making part of the recurrent weight matrix close to identity, thus forming kind of a longer term memory. We evaluate our model in language modeling experiments, where we obtain similar performance to the much more complex Long Short Term Memory (LSTM) networks (Hochreiter & Schmidhuber, 1997).

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 "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"

Connected through hyperedge HE-3 (induced by source paper "rethinking full connectivity in recurrent neural networks"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "sparse persistent rnns squeezing large recurrent networks on chip"
    - "efficient neural audio synthesis"
    - "an analysis of neural language modeling at multiple scales"
    - "predefined sparseness in recurrent sequence models"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "block sparse recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "language modeling through long term memory network"):
  with members:
    - "enhancing semantic word representations by embedding deeper word relationships"
    - "learning simpler language models with the differential state framework"
    - "data noising as smoothing in neural network language models"
    - "recent trends in deep learning based natural language processing"
    - "learning over long time lags"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "attention based models for speech recognition"
    - "ask me anything dynamic memory networks for natural language processing"

Connected through hyperedge HE-5 (induced by source paper "learning multi sense word distributions using approximate kullback leibler divergence"):
  with members:
    - "ukp athene multi sentence textual entailment for claim verification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"
    - "stance detection with bidirectional conditional encoding"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "experiments with three approaches to recognizing lexical entailment"
    - "word representations via gaussian embedding"

Connected through hyperedge HE-6 (induced by source paper "slim lstm networks lstm_6 and lstm_c6"):
  with members:
    - "performance of three slim variants of the long short term memory lstm layer"
    - "slim lstms"
    - "lstm a search space odyssey"
    - "simplified long short term memory recurrent neural networks part iii"
    - "simplified gating in long short term memory lstm recurrent neural networks"
    - "a basic recurrent neural network model"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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 "recurrence to the rescue towards causal spatiotemporal representations"):
  with members:
    - "massively parallel video networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"

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)