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: "improving zero shot translation of low resource languages"
  Abstract: Recent work on multilingual neural machine translation reported competitive performance with respect to bilingual models and surprisingly good performance even on (zeroshot) translation directions not observed at training time. We investigate here a zero-shot translation in a particularly lowresource multilingual setting. We propose a simple iterative training procedure that leverages a duality of translations directly generated by the system for the zero-shot directions. The translations produced by the system (sub-optimal since they contain mixed language from the shared vocabulary), are then used together with the original parallel data to feed and iteratively re-train the multilingual network. Over time, this allows the system to learn from its own generated and increasingly better output. Our approach shows to be effective in improving the two zero-shot directions of our multilingual model. In particular, we observed gains of about 9 BLEU points over a baseline multilingual model and up to 2.08 BLEU over a pivoting mechanism using two bilingual models. Further analysis shows that there is also a slight improvement in the non-zero-shot language directions.

Connected through hyperedge HE-1 (induced by source paper "a brief survey of multilingual neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "the missing ingredient in zero shot neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "zero resource multilingual model transfer learning what to share"

Connected through hyperedge HE-2 (induced by source paper "a survey of multilingual neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "the missing ingredient in zero shot neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "zero resource multilingual model transfer learning what to share"
    - "universal neural machine translation for extremely low resource languages"

Connected through hyperedge HE-3 (induced by source paper "multilingual neural machine translation for zero resource languages"):
  with members:
    - "self attention with relative position representations"
    - "deep neural machine translation with weakly recurrent units"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "nematus a toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "zero resource translation with multi lingual neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "generalized data augmentation for low resource translation"):
  with members:
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "choosing transfer languages for cross lingual learning"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "style transfer as unsupervised machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "unsupervised cross lingual information retrieval using monolingual data only"

Connected through hyperedge HE-5 (induced by source paper "pivot based transfer learning for neural machine translation between non english languages"):
  with members:
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "massively multilingual neural machine translation"
    - "filtering and mining parallel data in a joint multilingual space"
    - "trivial transfer learning for low resource neural machine translation"
    - "triangular architecture for rare language translation"
    - "a neural interlingua for multilingual machine translation"
    - "zero shot dual machine translation"
    - "understanding back translation at scale"

Connected through hyperedge HE-6 (induced by source paper "exploiting out of domain parallel data through multilingual transfer learning for low resource neural machine translation"):
  with members:
    - "unsupervised statistical machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "unsupervised neural machine translation initialized by unsupervised statistical machine translation"
    - "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "bi directional neural machine translation with synthetic parallel data"
    - "attention is all you need"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "multilingual nmt with a language independent attention bridge"):
  with members:
    - "universal neural machine translation for extremely low resource languages"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "a teacher student framework for zero resource neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "zero resource translation with multi lingual neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "contextual parameter generation for universal neural machine translation"):
  with members:
    - "universal neural machine translation for extremely low resource languages"
    - "extreme adaptation for personalized neural machine translation"
    - "a teacher student framework for zero resource neural machine translation"
    - "zero resource translation with multi lingual neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"
    - "sequence to sequence learning with neural networks"

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)