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 statistical machine translation for a resource poor language using related resource rich languages"
  Abstract: We propose a novel language-independent approach for improving machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X1 into a resourcerich language Y given a bi-text containing a limited number of parallel sentences for X1-Y and a larger bi-text for X2-Y for some resource-rich language X2 that is closely related to X1. This is achieved by taking advantage of the opportunities that vocabulary overlap and similarities between the languages X1 and X2 in spelling, word order, and syntax offer: (1) we improve the word alignments for the resource-poor language, (2) we further augment it with additional translation options, and (3) we take care of potential spelling differences through appropriate transliteration. The evaluation for Indonesian → English using Malay and for Spanish → English using Portuguese and pretending Spanish is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points, respectively, which is an improvement over the best rivaling approaches, while using much less additional data. Overall, our method cuts the amount of necessary "real" training data by a factor of 2-5.

Connected through hyperedge HE-1 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"

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 "augmenting statistical machine translation with subword translation of out of vocabulary words"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "enriching word vectors with subword information"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "transfer learning across low resource related languages for neural machine translation"):
  with members:
    - "six challenges for neural machine translation"
    - "transfer learning for low resource neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "role of morphology injection in smt a case study from indian language perspective"):
  with members:
    - "a case study on english malayalam machine translation"
    - "lexical resources for hindi marathi mt"

Connected through hyperedge HE-6 (induced by source paper "bridge correlational neural networks for multilingual multimodal representation learning"):
  with members:
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "correlational neural networks"
    - "leveraging monolingual data for crosslingual compositional word representations"
    - "multilingual models for compositional distributed semantics"
    - "caffe convolutional architecture for fast feature embedding"
    - "an autoencoder approach to learning bilingual word representations"
    - "exploiting similarities among languages for machine translation"
    - "multilingual distributed representations without word alignment"

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)