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: "learning to translate in real time with neural machine translation"
  Abstract: Translating in real-time, a.k.a. simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine translation methods. We propose a neural machine translation (NMT) framework for simultaneous translation in which an agent learns to make decisions on when to translate from the interaction with a pre-trained NMT environment. To trade off quality and delay, we extensively explore various targets for delay and design a method for beam-search applicable in the simultaneous MT setting. Experiments against state-of-the-art baselines on two language pairs demonstrate the efficacy of the proposed framework both quantitatively and qualitatively.

Connected through hyperedge HE-1 (induced by source paper "plug and play language models a simple approach to controlled text generation"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "the curious case of neural text degeneration"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fine tuning language models from human preferences"
    - "putting machine translation in context with the noisy channel model"

Connected through hyperedge HE-2 (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-3 (induced by source paper "text as environment a deep reinforcement learning text readability assessment model"):
  with members:
    - "reference less quality estimation of text simplification systems"
    - "learning when not to answer a ternary reward structure for reinforcement learning based question answering"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "towards diverse text generation with inverse reinforcement learning"
    - "a machine learning approach to persian text readability assessment using a crowdsourced dataset"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (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"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "the best of both worlds combining recent advances in neural machine translation"

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"
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "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"
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "monotonic chunkwise attention"
    - "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 "re translation strategies for long form simultaneous spoken language translation"):
  with members:
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "dutongchuan context aware translation model for simultaneous interpreting"
    - "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework"
    - "you may not need attention"
    - "low latency neural speech translation"
    - "can neural machine translation do simultaneous translation"

Connected through hyperedge HE-8 (induced by source paper "simultaneous neural machine translation using connectionist temporal classification"):
  with members:
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "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)