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: "strongly typed recurrent neural networks"
  Abstract: Recurrent neural networks are increasing popular models for sequential learning. Unfortunately, although the most effective RNN architectures are perhaps excessively complicated, extensive searches have not found simpler alternatives. This paper imports ideas from physics and functional programming into RNN design to provide guiding principles. From physics, we introduce type constraints, analogous to the constraints that forbids adding meters to seconds. From functional programming, we require that strongly-typed architectures factorize into stateless learnware and state-dependent firmware, reducing the impact of side-effects. The features learned by strongly-typed nets have a simple semantic interpretation via dynamic average-pooling on one-dimensional convolutions. We also show that strongly-typed gradients are better behaved than in classical architectures, and characterize the representational power of strongly-typed nets. Finally, experiments show that, despite being more constrained, strongly-typed architectures achieve lower training and comparable generalization error to classical architectures.

Connected through hyperedge HE-1 (induced by source paper "a lightweight recurrent network for sequence modeling"):
  with members:
    - "rational recurrences"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "accelerating neural transformer via an average attention network"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "simple recurrent units for highly parallelizable recurrence"
    - "deriving neural architectures from sequence and graph kernels"
    - "recurrent additive networks"

Connected through hyperedge HE-2 (induced by source paper "multi scale quasi rnn for next item recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "multi pointer co attention networks for recommendation"
    - "a simple convolutional generative network for next item recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"

Connected through hyperedge HE-3 (induced by source paper "multi task sentence encoding model for semantic retrieval in question answering systems"):
  with members:
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
    - "natural language inference over interaction space"
    - "attention is all you need"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a structured self attentive sentence embedding"
    - "bilateral multi perspective matching for natural language sentences"
    - "learning to compose task specific tree structures"

Connected through hyperedge HE-4 (induced by source paper "deep neural machine translation with weakly recurrent units"):
  with members:
    - "the best of both worlds combining recent advances in neural machine translation"
    - "simple recurrent units for highly parallelizable recurrence"
    - "deriving neural architectures from sequence and graph kernels"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "non autoregressive neural machine translation"
    - "convolutional sequence to sequence learning"
    - "deep architectures for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "long short term memory as a dynamically computed element wise weighted sum"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"
    - "recurrent additive networks"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "long short term memory networks for machine reading"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-6 (induced by source paper "benchmarking deep sequential models on volatility predictions for financial time series"):
  with members:
    - "a neural stochastic volatility model"
    - "fast slow recurrent neural networks"
    - "recurrent highway networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "pixel recurrent neural networks"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "deep learning in neural networks"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "sliced recurrent neural networks"):
  with members:
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "simple recurrent units for highly parallelizable recurrence"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "rational recurrences"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "an analysis of neural language modeling at multiple scales"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deriving neural architectures from sequence and graph kernels"
    - "lstm a search space odyssey"
    - "recurrent additive 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.LG(Machine Learning)