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 memory network for language modeling"
  Abstract: Recurrent Neural Networks (RNN) have obtained excellent result in many natural language processing (NLP) tasks. However, understanding and interpreting the source of this success remains a challenge. In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data. We demonstrate the power of RMN on language modeling and sentence completion tasks. On language modeling, RMN outperforms Long Short-Term Memory (LSTM) network on three large German, Italian, and English dataset. Additionally we perform in-depth analysis of various linguistic dimensions that RMN captures. On Sentence Completion Challenge, for which it is essential to capture sentence coherence, our RMN obtains 69.2% accuracy, surpassing the previous state-of-the-art by a large margin.

Connected through hyperedge HE-1 (induced by source paper "reflective decoding network for image captioning"):
  with members:
    - "neural baby talk"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "mattnet modular attention network for referring expression comprehension"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "attention is all you need"
    - "self attentive residual decoder for neural machine translation"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "improving neural language models by segmenting attending and predicting the future"):
  with members:
    - "fast parametric learning with activation memorization"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "efficient neural architecture search via parameter sharing"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-3 (induced by source paper "joint embedding learning of educational knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "knowledge forest a novel model to organize knowledge fragments"
    - "incorporating literals into knowledge graph embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "character word lstm language models"

Connected through hyperedge HE-4 (induced by source paper "r transformer recurrent neural network enhanced transformer"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "character level language modeling with deeper self attention"
    - "attention is all you need"
    - "dilated recurrent neural networks"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-5 (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"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

Connected through hyperedge HE-6 (induced by source paper "neural vector spaces for unsupervised information retrieval"):
  with members:
    - "neural networks for information retrieval"
    - "semantic entity retrieval toolkit"
    - "efficient vector representation for documents through corruption"
    - "pyndri a python interface to the indri search engine"
    - "reply with proactive recommendation of email attachments"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "noisy activation functions"

Connected through hyperedge HE-7 (induced by source paper "recurrent attention unit"):
  with members:
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "quasi recurrent neural networks"
    - "survey on the attention based rnn model and its applications in computer vision"
    - "effective approaches to attention based neural machine translation"
    - "skip thought vectors"
    - "show attend and tell neural image caption generation with visual attention"
    - "grid long short term memory"

Connected through hyperedge HE-8 (induced by source paper "continuous learning in a hierarchical multiscale neural network"):
  with members:
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "learning to generate reviews and discovering sentiment"
    - "regularizing and optimizing lstm language models"
    - "dynamic evaluation of neural sequence models"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "memory efficient backpropagation through time"

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)