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: "frustratingly short attention spans in neural language modeling"
  Abstract: Neural language models predict the next token using a latent representation of the immediate token history. Recently, various methods for augmenting neural language models with an attention mechanism over a differentiable memory have been proposed. For predicting the next token, these models query information from a memory of the recent history which can facilitate learning mid- and long-range dependencies. However, conventional attention mechanisms used in memory-augmented neural language models produce a single output vector per time step. This vector is used both for predicting the next token as well as for the key and value of a differentiable memory of a token history. In this paper, we propose a neural language model with a key-value attention mechanism that outputs separate representations for the key and value of a differentiable memory, as well as for encoding the next-word distribution. This model outperforms existing memory-augmented neural language models on two corpora. Yet, we found that our method mainly utilizes a memory of the five most recent output representations. This led to the unexpected main finding that a much simpler model based only on the concatenation of recent output representations from previous time steps is on par with more sophisticated memory-augmented neural language models.

Connected through hyperedge HE-1 (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"
    - "automatic rule extraction from long short term memory networks"
    - "dilated recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "lightweight and efficient neural natural language processing with quaternion networks"):
  with members:
    - "quaternion collaborative filtering for recommendation"
    - "quaternion convolutional neural networks"
    - "universal transformers"
    - "quaternion knowledge graph embedding"
    - "attending to mathematical language with transformers"
    - "quaternet a quaternion based recurrent model for human motion"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "go from the general to the particular multi domain translation with domain transformation networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multilingual neural machine translation with knowledge distillation"
    - "improving domain adaptation translation with domain invariant and specific information"
    - "massively multilingual adversarial speech recognition"
    - "a survey of domain adaptation for neural machine translation"
    - "domain adversarial training for accented speech recognition"
    - "translating pro drop languages with reconstruction models"
    - "adversarial multi criteria learning for chinese word segmentation"

Connected through hyperedge HE-4 (induced by source paper "an efficient model for sentiment analysis of electronic product reviews in vietnamese"):
  with members:
    - "structural attention neural networks for improved sentiment analysis"
    - "attention is all you need"
    - "squeeze and excitation networks"
    - "convolutional sequence to sequence learning"
    - "long short term memory networks for machine reading"
    - "enriching word vectors with subword information"
    - "hierarchical question image co attention for visual question answering"
    - "recurrent models of visual attention"

Connected through hyperedge HE-5 (induced by source paper "ensemble approach for natural language question answering problem"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "pay more attention neural architectures for question answering"
    - "comparative study of cnn and rnn for natural language processing"
    - "dcn mixed objective and deep residual coattention for question answering"
    - "bidirectional attention flow for machine comprehension"
    - "effective approaches to attention based neural machine translation"
    - "efficient estimation of word representations in vector space"
    - "popular ensemble methods an empirical study"

Connected through hyperedge HE-6 (induced by source paper "deep sequential models for suicidal ideation from multiple source data"):
  with members:
    - "structured attention networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "character level language modeling with deeper self attention"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "sparse attentive backtracking long range credit assignment in recurrent networks"
    - "unbiasing truncated backpropagation through time"
    - "improving gans using optimal transport"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "learning to generate reviews and discovering sentiment"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "middle out decoding"):
  with members:
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "neural program search solving programming tasks from description and examples"
    - "texygen a benchmarking platform for text generation models"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning structured text representations"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "towards diverse and natural image descriptions via a conditional gan"

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)