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 "trainable greedy decoding for neural machine translation"):
  Abstract: Recent research in neural machine translation has largely focused on two aspects; neural network architectures and end-to-end learning algorithms. The problem of decoding, however, has received relatively little attention from the research community. In this paper, we solely focus on the problem of decoding given a trained neural machine translation model. Instead of trying to build a new decoding algorithm for any specific decoding objective, we propose the idea of trainable decoding algorithm in which we train a decoding algorithm to find a translation that maximizes an arbitrary decoding objective. More specifically, we design an actor that observes and manipulates the hidden state of the neural machine translation decoder and propose to train it using a variant of deterministic policy gradient. We extensively evaluate the proposed algorithm using four language pairs and two decoding objectives and show that we can indeed train a trainable greedy decoder that generates a better translation (in terms of a target decoding objective) with minimal computational overhead.

Includes members:
  Member "learning to decode for future success":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation")
  Member "character based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "sequence to sequence learning as beam search optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")
  Member "a simple fast diverse decoding algorithm for neural generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "non autoregressive neural dialogue generation")
  Member "learning to translate in real time with neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plug and play language models a simple approach to controlled text generation")
  Member "neural machine translation with reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bi decoder augmented network for neural machine translation")
  Member "toward multilingual neural machine translation with universal encoder and decoder":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "achieving open vocabulary neural machine translation with hybrid word character models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "authorship attribution in bangla literature using character level cnn")

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)