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: "interactive attention for neural machine translation"
  Abstract: Conventional attention-based Neural Machine Translation (NMT) conducts dynamic alignment in generating the target sentence. By repeatedly reading the representation of source sentence, which keeps fixed after generated by the encoder (Bahdanau et al., 2015), the attention mechanism has greatly enhanced state-of-the-art NMT. In this paper, we propose a new attention mechanism, called INTERACTIVE ATTENTION, which models the interaction between the decoder and the representation of source sentence during translation by both reading and writing operations. INTERACTIVE ATTENTION can keep track of the interaction history and therefore improve the translation performance. Experiments on NIST Chinese-English translation task show that INTERACTIVE ATTENTION can achieve significant improvements over both the previous attention-based NMT baseline and some state-of-the-art variants of attention-based NMT (i.e., coverage models (Tu et al., 2016)). And neural machine translator with our INTERACTIVE ATTENTION can outperform the open source attention-based NMT system Groundhog by 4.22 BLEU points and the open source phrase-based system Moses by 3.94 BLEU points averagely on multiple test sets.

Connected through hyperedge HE-1 (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"
    - "information aggregation for multi head attention with routing by agreement"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "improving bidirectional decoding with dynamic target semantics in neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "synchronous bidirectional inference for neural sequence generation"
    - "achieving human parity on automatic chinese to english news translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "neural machine translation with key value memory augmented attention"

Connected through hyperedge HE-3 (induced by source paper "table to text describing table region with natural language"):
  with members:
    - "challenges in data to document generation"
    - "pointing the unknown words"
    - "deep reinforcement learning for dialogue generation"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural text generation from structured data with application to the biography domain"
    - "modeling coverage for neural machine translation"
    - "sequence to sequence rnns for text summarization"

Connected through hyperedge HE-4 (induced by source paper "deconvolution based global decoding for neural machine translation"):
  with members:
    - "global encoding for abstractive summarization"
    - "decoding history based adaptive control of attention for neural machine translation"
    - "label embedding network learning label representation for soft training of deep networks"
    - "bag of words as target for neural machine translation"
    - "attention is all you need"
    - "deconvolutional latent variable model for text sequence matching"
    - "get to the point summarization with pointer generator networks"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "variational recurrent neural machine translation"):
  with members:
    - "a gru gated attention model for neural machine translation"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "deep neural machine translation with linear associative unit"
    - "memory enhanced decoder for neural machine translation"
    - "variational neural machine translation"
    - "modeling coverage for neural machine translation"
    - "neural machine translation with recurrent attention modeling"

Connected through hyperedge HE-6 (induced by source paper "bag of words as target for neural machine translation"):
  with members:
    - "decoding history based adaptive control of attention for neural machine translation"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "future prediction based model for neural machine translation"):
  with members:
    - "decoding history based adaptive control of attention for neural machine translation"
    - "bag of words as target for neural machine translation"
    - "learning when to concentrate or divert attention self adaptive attention temperature for neural machine translation"
    - "attention is all you need"
    - "multi channel encoder for neural machine translation"
    - "coverage embedding models for neural machine translation"
    - "neural machine translation in linear time"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "neural machine translation with key value memory augmented attention"):
  with members:
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "memory augmented neural machine translation"
    - "search engine guided non parametric neural machine translation"
    - "neural machine translation advised by statistical machine translation"
    - "memory enhanced decoder for neural machine translation"
    - "implicit distortion and fertility models for attention based encoder decoder nmt model"
    - "coverage embedding models for neural machine translation"

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)