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: "low latency neural speech translation"
  Abstract: Through the development of neural machine translation, the quality of machine translation systems has been improved significantly. By exploiting advancements in deep learning, systems are now able to better approximate the complex mapping from source sentences to target sentences. But with this ability, new challenges also arise. An example is the translation of partial sentences in low-latency speech translation. Since the model has only seen complete sentences in training, it will always try to generate a complete sentence, though the input may only be a partial sentence. We show that NMT systems can be adapted to scenarios where no task-specific training data is available. Furthermore, this is possible without losing performance on the original training data. We achieve this by creating artificial data and by using multi-task learning. After adaptation, we are able to reduce the number of corrections displayed during incremental output construction by 45%, without a decrease in translation quality.

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"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"

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 "mixed signal charge domain acceleration of deep neural networks through interleaved bit partitioned arithmetic"):
  with members:
    - "development of a sensory neural network for medical diagnosing"
    - "yolov3 an incremental improvement"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "recogym a reinforcement learning environment for the problem of product recommendation in online advertising"
    - "safedrive enhancing lane appearance for autonomous and assisted driving under limited visibility"
    - "caterpillar coarse grain reconfigurable architecture for accelerating the training of deep neural networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

Connected through hyperedge HE-4 (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"
    - "learning to translate in real time with neural machine translation"
    - "can neural machine translation do simultaneous 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)