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: "can neural machine translation do simultaneous translation"
  Abstract: We investigate the potential of attention-based neural machine translation in simultaneous translation. We introduce a novel decoding algorithm, called simultaneous greedy decoding, that allows an existing neural machine translation model to begin translating before a full source sentence is received. This approach is unique from previous works on simultaneous translation in that segmentation and translation are done jointly to maximize the translation quality and that translating each segment is strongly conditioned on all the previous segments. This paper presents a first step toward building a full simultaneous translation system based on neural machine translation.

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 "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-3 (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"
    - "learning to translate in real time with neural machine translation"
    - "neural machine translation of rare words with subword units"

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"
    - "low latency neural speech translation"
    - "learning to translate in real time with neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "simpler and faster learning of adaptive policies for simultaneous translation"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "speculative beam search for simultaneous translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-6 (induced by source paper "incremental natural language processing challenges strategies and evaluation"):
  with members:
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "learning to translate in real time with neural machine translation"
    - "an incremental parser for abstract meaning representation"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"
    - "strongly incremental repair detection"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "incremental decoding and training methods for simultaneous translation in neural machine translation"):
  with members:
    - "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"
    - "neural machine translation training in a multi domain scenario"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "learning to translate in real time with neural machine translation"):
  with members:
    - "online segment to segment neural transduction"
    - "learning online alignments with continuous rewards policy gradient"
    - "a neural transducer"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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)