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: "recurrent additive networks"
  Abstract: We introduce recurrent additive networks (RANs), a new gated RNN which is distinguished by the use of purely additive latent state updates. At every time step, the new state is computed as a gated component-wise sum of the input and the previous state, without any of the non-linearities commonly used in RNN transition dynamics. We formally show that RAN states are weighted sums of the input vectors, and that the gates only contribute to computing the weights of these sums. Despite this relatively simple functional form, experiments demonstrate that RANs perform on par with LSTMs on benchmark language modeling problems. This result shows that many of the non-linear computations in LSTMs and related networks are not essential, at least for the problems we consider, and suggests that the gates are doing more of the computational work than previously understood.

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"
    - "breaking the softmax bottleneck a high rank rnn language model"

Connected through hyperedge HE-2 (induced by source paper "an augmented transformer architecture for natural language generation tasks"):
  with members:
    - "self attention with relative position representations"
    - "allennlp a deep semantic natural language processing platform"
    - "end to end dense video captioning with masked transformer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "bas an answer selection method using bert language model"):
  with members:
    - "a compare aggregate model with latent clustering for answer selection"
    - "attention based pairwise multi perspective convolutional neural network for answer selection in question answering"
    - "albert a lite bert for self supervised learning of language representations"
    - "deep learning using rectified linear units relu"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "exploring the effectiveness of convolutional neural networks for answer selection in end to end question answering"
    - "applying deep learning to answer selection a study and an open task"

Connected through hyperedge HE-4 (induced by source paper "bridging the knowledge gap enhancing question answering with world and domain knowledge"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "one shot relational learning for knowledge graphs"
    - "attention is all you need"
    - "semi supervised sequence tagging with bidirectional language models"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "squad 100 000 questions for machine comprehension of text"
    - "semi supervised sequence learning"

Connected through hyperedge HE-5 (induced by source paper "end to end optimized transmission over dispersive intensity modulated channels using bidirectional recurrent neural networks"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "achievable information rates for nonlinear fiber communication via end to end autoencoder learning"
    - "an introduction to deep learning for the physical layer"
    - "applying neural networks in optical communication systems possible pitfalls"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "simplifying neural machine translation with addition subtraction twin gated recurrent networks"):
  with members:
    - "asynchronous bidirectional decoding for neural machine translation"
    - "variational recurrent neural machine translation"
    - "accelerating neural transformer via an average attention network"
    - "attention is all you need"
    - "a gru gated attention model for neural machine translation"
    - "modeling past and future for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"

Connected through hyperedge HE-7 (induced by source paper "pyramidal recurrent unit for language modeling"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "alternating multi bit quantization for recurrent neural networks"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "factorization tricks for lstm networks"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "sopa bridging cnns rnns and weighted finite state machines"):
  with members:
    - "recurrent neural networks as weighted language recognizers"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "convolutional sequence to sequence learning"
    - "quasi recurrent neural networks"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through representation erasure"
    - "improving hypernymy detection with an integrated path based and distributional method"

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.CL(Computation and Language)