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: "distance based self attention network for natural language inference"
  Abstract: Attention mechanism has been used as an ancillary means to help RNN or CNN. However, the Transformer (Vaswani et al., 2017) recently recorded the state-of-the-art performance in machine translation with a dramatic reduction in training time by solely using attention. Motivated by the Transformer, Directional Self Attention Network (Shen et al., 2017), a fully attention-based sentence encoder, was proposed. It showed good performance with various data by using forward and backward directional information in a sentence. But in their study, not considered at all was the distance between words, an important feature when learning the local dependency to help understand the context of input text. We propose Distance-based Self-Attention Network, which considers the word distance by using a simple distance mask in order to model the local dependency without losing the ability of modeling global dependency which attention has inherent. Our model shows good performance with NLI data, and it records the new state-of-the-art result with SNLI data. Additionally, we show that our model has a strength in long sentences or documents.

Connected through hyperedge HE-1 (induced by source paper "textnas a neural architecture search space tailored for text representation"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "deep contextualized word representations"
    - "darts differentiable architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enhancing sentence embedding with generalized pooling"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "natural language inference with hierarchical bilstm max pooling architecture"

Connected through hyperedge HE-2 (induced by source paper "self attentional models for lattice inputs"):
  with members:
    - "hybrid self attention network for machine translation"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "graph convolutional networks for named entity recognition"
    - "attention is all you need"
    - "on the fly operation batching in dynamic computation graphs"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "towards open intent discovery for conversational text"):
  with members:
    - "joint slot filling and intent detection via capsule neural networks"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "zero shot user intent detection via capsule neural networks"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "multi domain adversarial learning for slot filling in spoken language understanding"

Connected through hyperedge HE-4 (induced by source paper "syntax infused transformer and bert models for machine translation and natural language understanding"):
  with members:
    - "improving deep transformer with depth scaled initialization and merged attention"
    - "self attention with relative position representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi head attention with disagreement regularization"
    - "attention is all you need"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "snu_ids at semeval 2019 task 3 addressing training test class distribution mismatch in conversational classification"):
  with members:
    - "deep contextualized word representations"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "focal loss for dense object detection"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "emoji2vec learning emoji representations from their description"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "tensorized self attention efficiently modeling pairwise and global dependencies together"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "generating wikipedia by summarizing long sequences"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "enhancing sentence embedding with generalized pooling"
    - "natural language inference with hierarchical bilstm max pooling architecture"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-7 (induced by source paper "combining similarity features and deep representation learning for stance detection in the context of checking fake news"):
  with members:
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "universal transformers"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "automatic stance detection using end to end memory networks"
    - "co stack residual affinity networks with multi level attention refinement for matching text sequences"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "debunking fake news one feature at a time"

Connected through hyperedge HE-8 (induced by source paper "natural language inference with hierarchical bilstm max pooling architecture"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "enhancing sentence embedding with generalized pooling"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "testing the generalization power of neural network models across nli benchmarks"
    - "senteval an evaluation toolkit for universal sentence representations"

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)