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: "information aggregation for multi head attention with routing by agreement"
  Abstract: Multi-head attention is appealing for its ability to jointly extract different types of information from multiple representation subspaces. Concerning the information aggregation, a common practice is to use a concatenation followed by a linear transformation, which may not fully exploit the expressiveness of multi-head attention. In this work, we propose to improve the information aggregation for multi-head attention with a more powerful routing-by-agreement algorithm. Specifically, the routing algorithm iteratively updates the proportion of how much a part (i.e. the distinct information learned from a specific subspace) should be assigned to a whole (i.e. the final output representation), based on the agreement between parts and wholes. Experimental results on linguistic probing tasks and machine translation tasks prove the superiority of the advanced information aggregation over the standard linear transformation.

Connected through hyperedge HE-1 (induced by source paper "assessing the ability of self attention networks to learn word order"):
  with members:
    - "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"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "dynamic past and future for neural machine translation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "neural machine translation with adequacy oriented learning"
    - "deconvolution based global decoding for neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "investigating capsule networks with dynamic routing for text classification"
    - "information aggregation via dynamic routing for sequence encoding"
    - "a hybrid word character model for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "generating diverse translation by manipulating multi head attention"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "investigating backtranslation in neural machine translation"
    - "d page diverse paraphrase generation"
    - "multi head attention with disagreement regularization"
    - "convolutional self attention network"
    - "sequence to sequence mixture model for diverse machine translation"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-4 (induced by source paper "improving multi head attention with capsule networks"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "weighted transformer network for machine translation"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "investigating capsule networks with dynamic routing for text classification"
    - "self attention with relative position representations"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "scaling neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "exploiting sentential context for neural machine translation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "context aware self attention networks"
    - "universal transformers"
    - "modeling recurrence for transformer"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-6 (induced by source paper "neuron interaction based representation composition for neural machine translation"):
  with members:
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "exploiting deep representations for neural machine translation"
    - "weighted transformer network for machine translation"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi head attention with disagreement regularization"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-7 (induced by source paper "multi granularity self attention for neural machine translation"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "area attention"
    - "strategies for structuring story generation"
    - "towards better modeling hierarchical structure for self attention with ordered neurons"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "modeling recurrence for transformer"
    - "phrase based attentions"
    - "bi directional block self attention for fast and memory efficient sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "capturing argument interaction in semantic role labeling with capsule networks"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "polyglot semantic role labeling"
    - "multi labeled relation extraction with attentive capsule network"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "a full end to end semantic role labeler syntax agnostic over syntax aware"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "deep contextualized word representations"
    - "information aggregation via dynamic routing for sequence encoding"

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)