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: "incremental decoding and training methods for simultaneous translation in neural machine translation"
  Abstract: We address the problem of simultaneous translation by modifying the Neural MT decoder to operate with dynamically built encoder and attention. We propose a tunable agent which decides the best segmentation strategy for a user-defined BLEU loss and Average Proportion (AP) constraint. Our agent outperforms previously proposed Wait-if-diff and Wait-if-worse agents (Cho and Esipova, 2016) on BLEU with a lower latency. Secondly we proposed data-driven changes to Neural MT training to better match the incremental decoding framework.

Connected through hyperedge HE-1 (induced by source paper "learning coupled policies for simultaneous machine translation"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "thinking slow about latency evaluation for simultaneous machine translation"
    - "bridging the gap between training and inference for neural machine translation"
    - "recent advances in imitation learning from observation"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "direct speech to speech translation with a sequence to sequence model"
    - "low latency neural speech translation"

Connected through hyperedge HE-2 (induced by source paper "speculative beam search for simultaneous translation"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "learning to stop in structured prediction for neural machine translation"
    - "correcting length bias in neural machine translation"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "monotonic multihead attention"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework"
    - "a call for clarity in reporting bleu scores"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "online and linear time attention by enforcing monotonic alignments"

Connected through hyperedge HE-4 (induced by source paper "assessing incrementality in sequence to sequence models"):
  with members:
    - "deep text to speech system with seq2seq model"
    - "transcoding compositionally using attention to find more generalizable solutions"
    - "on the convergence of adam and beyond"
    - "learning compositionally through attentive guidance"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "closed book training to improve summarization encoder memory"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-5 (induced by source paper "simultaneous translation with flexible policy via restricted imitation learning"):
  with members:
    - "learning to stop in structured prediction for neural machine translation"
    - "you may not need attention"
    - "multi reference training with pseudo references for neural translation and text generation"
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "group sparse cnns for question classification with answer sets"

Connected through hyperedge HE-6 (induced by source paper "how to do simultaneous translation better with consecutive neural machine translation"):
  with members:
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "learning to translate in real time with neural machine translation"
    - "can neural machine translation do simultaneous translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-7 (induced by source paper "monotonic infinite lookback attention for simultaneous machine translation"):
  with members:
    - "thinking slow about latency evaluation for simultaneous machine translation"
    - "you may not need attention"
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "monotonic chunkwise attention"
    - "learning to translate in real time with neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"

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)