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 "diversity by phonetics and its application in neural machine translation"):
  Abstract: We introduce a powerful approach for Neural Machine Translation (NMT), whereby, during training and testing, together with the input we provide its phonetic encoding and the variants of such an encoding. This way we obtain very significant improvements up to 4 BLEU points over the state-of-the-art large-scale system. The phonetic encoding is the first part of our contribution, with a second being a theory that aims to understand the reason for this improvement. Our hypothesis states that the phonetic encoding helps NMT because it encodes a procedure to emphasize the difference between semantically diverse sentences. We conduct an empirical geometric validation of our hypothesis in support of which we obtain overwhelming evidence. Subsequently, as our third contribution and based on our theory, we develop artificial mechanisms that leverage during learning the hypothesized (and verified) effect phonetics. We achieve significant and consistent improvements overall language pairs and datasets: French-English, German-English, and Chinese-English in medium task IWSLT'17 and French-English in large task WMT'18 Bio, with up to 4 BLEU points over the state-of-the-art. Moreover, our approaches are more robust than baselines when evaluated on unknown out-of-domain test sets with up to a 5 BLEU point increase.

Includes members:
  Member "towards robust neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust unsupervised neural machine translation with adversarial training")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "stronger baselines for trustable results in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi layer representation fusion for neural machine translation")
  Member "convolutional sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "linear algebraic structure of word senses with applications to polysemy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "factored neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation")
  Member "linguistic input features improve neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "augmenting neural machine translation with knowledge graphs")
  Member "multi source neural translation":
    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)