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: "higher order recurrent neural networks"
  Abstract: In this paper, we study novel neural network structures to better model long term dependency in sequential data. We propose to use more memory units to keep track of more preceding states in recurrent neural networks (RNNs), which are all recurrently fed to the hidden layers as feedback through different weighted paths. By extending the popular recurrent structure in RNNs, we provide the models with better short-term memory mechanism to learn long term dependency in sequences. Analogous to digital filters in signal processing, we call these structures as higher order RNNs (HORNNs). Similar to RNNs, HORNNs can also be learned using the back-propagation through time method. HORNNs are generally applicable to a variety of sequence modelling tasks. In this work, we have examined HORNNs for the language modeling task using two popular data sets, namely the Penn Treebank (PTB) and English text8 data sets. Experimental results have shown that the proposed HORNNs yield the state-of-the-art performance on both data sets, significantly outperforming the regular RNNs as well as the popular LSTMs.

Connected through hyperedge HE-1 (induced by source paper "convolutional tensor train lstm for spatio temporal learning"):
  with members:
    - "a tensorized transformer for language modeling"
    - "efficient n dimensional convolutions via higher order factorization"
    - "robust deep networks with randomized tensor regression layers"
    - "scaling autoregressive video models"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (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"

Connected through hyperedge HE-3 (induced by source paper "non local recurrent neural memory for supervised sequence modeling"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "relational recurrent neural networks"
    - "high order recurrent neural networks for acoustic modelling"
    - "glomo unsupervisedly learned relational graphs as transferable representations"
    - "learning to generate reviews and discovering sentiment"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "modeling local dependence in natural language with multi channel recurrent neural networks"):
  with members:
    - "structured attention networks"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "dilated recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "high order recurrent neural networks for acoustic modelling"):
  with members:
    - "improved tdnns using deep kernels and frequency dependent grid rnns"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "pixel recurrent neural networks"
    - "deep residual learning for image recognition"
    - "grid long short term memory"
    - "highway long short term memory rnns for distant speech recognition"

Connected through hyperedge HE-6 (induced by source paper "global context neural machine translation through target side attentive residual connections"):
  with members:
    - "recurrent additive networks"
    - "predicting target language ccg supertags improves neural machine translation"
    - "learning structured text representations"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "towards string to tree neural machine translation"
    - "rra recurrent residual attention for sequence learning"

Connected through hyperedge HE-7 (induced by source paper "frustratingly short attention spans in neural language modeling"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "long short term memory networks for machine reading"
    - "text understanding with the attention sum reader network"
    - "consensus attention based neural networks for chinese reading comprehension"
    - "key value memory networks for directly reading documents"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "reference aware language models"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-8 (induced by source paper "dual path networks"):
  with members:
    - "memory efficient implementation of densenets"
    - "sharing residual units through collective tensor factorization in deep neural networks"
    - "mask r cnn"
    - "stacked hourglass networks for human pose estimation"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "places an image database for deep scene understanding"

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)