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: "you may not need attention"
  Abstract: In NMT, how far can we get without attention and without separate encoding and decoding? To answer that question, we introduce a recurrent neural translation model that does not use attention and does not have a separate encoder and decoder. Our eager translation model is low-latency, writing target tokens as soon as it reads the first source token, and uses constant memory during decoding. It performs on par with the standard attention-based model of Bahdanau et al. (2014), and better on long sentences.

Connected through hyperedge HE-1 (induced by source paper "retrieving sequential information for non autoregressive neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "a study of reinforcement learning for neural machine translation"
    - "fast decoding in sequence models using discrete latent variables"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "bag of words as target for neural machine translation"
    - "speeding up neural machine translation decoding by cube pruning"
    - "accelerating neural transformer via an average attention network"

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"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"

Connected through hyperedge HE-3 (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"
    - "multi reference training with pseudo references for neural translation and text generation"
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "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 "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"
    - "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"

Connected through hyperedge HE-5 (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"
    - "low latency neural speech translation"
    - "learning to translate in real time with neural machine translation"
    - "can neural machine translation do simultaneous translation"

Connected through hyperedge HE-6 (induced by source paper "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for 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"
    - "learning to translate in real time with neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "stacl simultaneous translation with integrated anticipation and controllable latency"):
  with members:
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "attention is all you need"
    - "learning to translate in real time with neural machine translation"
    - "an online sequence to sequence model using partial conditioning"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"

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)