human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "memory augmented neural machine translation"):
  Abstract: Neural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has limitations with handling infrequent words and word pairs. This paper presents a novel memory-augmented NMT (M-NMT) architecture, which stores knowledge about how words (usually infrequently encountered ones) should be translated in a memory and then utilizes them to assist the neural model. We use this memory mechanism to combine the knowledge learned from a conventional statistical machine translation system and the rules learned by an NMT system, and also propose a solution for out-of-vocabulary (OOV) words based on this framework. Our experiments on two Chinese-English translation tasks demonstrated that the M-NMT architecture outperformed the NMT baseline by $9.0$ and $2.7$ BLEU points on the two tasks, respectively. Additionally, we found this architecture resulted in a much more effective OOV treatment compared to competitive methods.

Includes members:
  Member "flexible and creative chinese poetry generation using neural memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixpoet diverse poetry generation via learning controllable mixed latent space")
  Member "memory enhanced decoder for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method")
  Member "a coverage embedding model for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "apply chinese radicals into neural machine translation deeper than character level")
  Member "neural versus phrase based machine translation quality a case study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "syntactically guided neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "incorporating structural alignment biases into an attentional neural translation model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an empirical study of spatial attention mechanisms in deep networks")
  Member "incorporating discrete translation lexicons into neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "latent relation language models")
  Member "google s neural machine translation system bridging the gap between human and machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "automl zero evolving machine learning algorithms from scratch")

Question: Please tell me which class the source paper 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)