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: "stacl simultaneous translation with integrated anticipation and controllable latency"
  Abstract: Simultaneous translation, which translates sentences before they are finished, is useful in many scenarios but is notoriously difficult due to word-order differences and simultaneity requirements. We introduce a very simple yet surprisingly effective `wait-k' model trained to generate the target sentence concurrently with the source sentence, but always k words behind, for any given k. This framework seamlessly integrates anticipation and translation in a single model that involves only minor changes to the existing neural translation framework. Experiments on Chinese-to-English simultaneous translation achieve a 5-word latency with 3.4 (single-ref) BLEU points degradation in quality compared to full-sentence non-simultaneous translation. We also formulate a new latency metric that addresses deficiencies in previous ones.

Connected through hyperedge HE-1 (induced by source paper "dutongchuan context aware translation model for simultaneous interpreting"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "end to end speech translation with knowledge distillation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "towards robust neural machine translation"
    - "modeling coherence for discourse neural machine translation"
    - "you may not need attention"

Connected through hyperedge HE-2 (induced by source paper "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "improving robustness of machine translation with synthetic noise"
    - "findings of the first shared task on machine translation robustness"
    - "end to end speech translation with knowledge distillation"
    - "a survey of domain adaptation for neural machine translation"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "multi reference training with pseudo references for neural translation and text generation"

Connected through hyperedge HE-3 (induced by source paper "learning to stop in structured prediction for neural machine translation"):
  with members:
    - "multi reference training with pseudo references for neural translation and text generation"
    - "ensemble sequence level training for multimodal mt osu baidu wmt18 multimodal machine translation system report"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria 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"
    - "opennmt open source toolkit for neural machine translation"
    - "group sparse cnns for question classification with answer sets"

Connected through hyperedge HE-4 (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"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "you may not need attention"
    - "multi reference training with pseudo references for neural translation and text generation"
    - "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-5 (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"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "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-6 (induced by source paper "monotonic infinite lookback attention for simultaneous machine translation"):
  with members:
    - "thinking slow about latency evaluation for simultaneous machine translation"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "you may not need attention"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "simultaneous neural machine translation using connectionist temporal classification"):
  with members:
    - "learning to translate in real time with neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "thinking slow about latency evaluation for simultaneous machine translation"):
  with members:
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "learning to translate in real time with 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)