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 "unsupervised neural machine translation"):
  Abstract: In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs. There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but they still require a strong cross-lingual signal. In this work, we completely remove the need of parallel data and propose a novel method to train an NMT system in a completely unsupervised manner, relying on nothing but monolingual corpora. Our model builds upon the recent work on unsupervised embedding mappings, and consists of a slightly modified attentional encoder-decoder model that can be trained on monolingual corpora alone using a combination of denoising and backtranslation. Despite the simplicity of the approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014 French-to-English and German-to-English translation. The model can also profit from small parallel corpora, and attains 21.81 and 15.24 points when combined with 100,000 parallel sentences, respectively. Our implementation is released as an open source project.

Includes members:
  Member "a teacher student framework for zero resource neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "six challenges for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "toward multilingual neural machine translation with universal encoder and decoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "unsupervised pretraining for sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "towards cross lingual distributed representations without parallel text trained with adversarial autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross lingual alignment vs joint training a comparative study and a simple unified framework")
  Member "multi way multilingual neural machine translation with a shared attention mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "learning distributed representations of sentences from unlabelled data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "contextual lensing of universal sentence representations")
  Member "fully character level neural machine translation without explicit segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a brief survey of multilingual neural machine translation")

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)