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: "dynamic self attention computing attention over words dynamically for sentence embedding"
  Abstract: In this paper, we propose Dynamic Self-Attention (DSA), a new self-attention mechanism for sentence embedding. We design DSA by modifying dynamic routing in capsule network (Sabouretal.,2017) for natural language processing. DSA attends to informative words with a dynamic weight vector. We achieve new state-of-the-art results among sentence encoding methods in Stanford Natural Language Inference (SNLI) dataset with the least number of parameters, while showing comparative results in Stanford Sentiment Treebank (SST) dataset.

Connected through hyperedge HE-1 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching"):
  with members:
    - "multi step retriever reader interaction for scalable open domain question answering"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "phrase indexed question answering a new challenge for scalable document comprehension"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "coarse grain fine grain coattention network for multi evidence question answering"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "efficient and robust question answering from minimal context over documents"
    - "neural models for reasoning over multiple mentions using coreference"
    - "global locally self attentive dialogue state tracker"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-3 (induced by source paper "multi scale self attention for text classification"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "star transformer"
    - "sequence labeling a practical approach"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "star transformer"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "sequence labeling a practical approach"
    - "multi task learning over graph structures"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "contextualized non local neural networks for sequence learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-5 (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"
    - "natural language inference with hierarchical bilstm max pooling architecture"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-6 (induced by source paper "sentence embeddings in nli with iterative refinement encoders"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "enhancing sentence embedding with generalized pooling"
    - "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"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-7 (induced by source paper "infusing knowledge into the textual entailment task using graph convolutional networks"):
  with members:
    - "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"
    - "multi task deep neural networks for natural language understanding"
    - "sentence embeddings in nli with iterative refinement encoders"
    - "roberta a robustly optimized bert pretraining approach"
    - "i know what you want semantic learning for text comprehension"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-8 (induced by source paper "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enhancing sentence embedding with generalized pooling"
    - "what if we simply swap the two text fragments a straightforward yet effective way to test the robustness of methods to confounding signals in nature language inference tasks"
    - "stress test evaluation for natural language inference"

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)