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: "thinking slow about latency evaluation for simultaneous machine translation"
  Abstract: Simultaneous machine translation attempts to translate a source sentence before it is finished being spoken, with applications to translation of spoken language for live streaming and conversation. Since simultaneous systems trade quality to reduce latency, having an effective and interpretable latency metric is crucial. We introduce a variant of the recently proposed Average Lagging (AL) metric, which we call Differentiable Average Lagging (DAL). It distinguishes itself by being differentiable and internally consistent to its underlying mathematical model.

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"
    - "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"
    - "low latency neural speech translation"

Connected through hyperedge HE-2 (induced by source paper "monotonic infinite lookback attention for simultaneous machine translation"):
  with members:
    - "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"
    - "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)