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: "modeling localness for self attention networks"
  Abstract: Self-attention networks have proven to be of profound value for its strength of capturing global dependencies. In this work, we propose to model localness for self-attention networks, which enhances the ability of capturing useful local context. We cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised distribution. To maintain the strength of capturing long distance dependencies and enhance the ability of capturing short-range dependencies, we only apply localness modeling to lower layers of self-attention networks. Quantitative and qualitative analyses on Chinese-English and English-German translation tasks demonstrate the effectiveness and universality of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "gret global representation enhanced transformer"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "context aware self attention networks"
    - "dynamic past and future for neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "global encoding for abstractive summarization"
    - "investigating capsule networks with dynamic routing for text classification"

Connected through hyperedge HE-2 (induced by source paper "local contextual attention with hierarchical structure for dialogue act recognition"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "self attention with relative position representations"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "a deep sequential model for discourse parsing on multi party dialogues"
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dialogue act recognition via crf attentive structured network"

Connected through hyperedge HE-3 (induced by source paper "explicit sparse transformer concentrated attention through explicit selection"):
  with members:
    - "adaptively sparse transformers"
    - "muse parallel multi scale attention for sequence to sequence learning"
    - "pay less attention with lightweight and dynamic convolutions"
    - "sparse sequence to sequence models"
    - "joint source target self attention with locality constraints"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "universal transformers"

Connected through hyperedge HE-4 (induced by source paper "on identifiability in transformers"):
  with members:
    - "attention is not not explanation"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "attention is not all you need for commonsense reasoning"
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "adding interpretable attention to neural translation models improves word alignment"

Connected through hyperedge HE-5 (induced by source paper "assessing the ability of self attention networks to learn word order"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "modeling recurrence for transformer"
    - "towards robust neural machine translation"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "context aware self attention networks"):
  with members:
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "convolutional self attention network"

Connected through hyperedge HE-8 (induced by source paper "two headed monster and crossed co attention networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "context aware self attention networks"
    - "modeling recurrence for transformer"
    - "adaptive attention span in transformers"
    - "scaling neural machine translation"
    - "multi pointer co attention networks for recommendation"

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)