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: "meta learning for low resource neural machine translation"
  Abstract: In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML) for low-resource neural machine translation (NMT). We frame low-resource translation as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks. We use the universal lexical representation~\citep{gu2018universal} to overcome the input-output mismatch across different languages. We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro, Lv, Fi, Tr and Ko) as target tasks. We show that the proposed approach significantly outperforms the multilingual, transfer learning based approach~\citep{zoph2016transfer} and enables us to train a competitive NMT system with only a fraction of training examples. For instance, the proposed approach can achieve as high as 22.04 BLEU on Romanian-English WMT'16 by seeing only 16,000 translated words (~600 parallel sentences).

Connected through hyperedge HE-1 (induced by source paper "automated relational meta learning"):
  with members:
    - "few shot unsupervised image to image translation"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "tafe net task aware feature embeddings for low shot learning"
    - "metapred meta learning for clinical risk prediction with limited patient electronic health records"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "hierarchically structured meta learning"
    - "on first order meta learning algorithms"
    - "toward multimodal model agnostic meta learning"

Connected through hyperedge HE-2 (induced by source paper "learning to few shot learn across diverse natural language classification tasks"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "bert rediscovers the classical nlp pipeline"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "learning and evaluating general linguistic intelligence"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "multi task deep neural networks for natural language understanding"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-3 (induced by source paper "multilingual neural machine translation with knowledge distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "sentence wise smooth regularization for sequence to sequence learning"
    - "universal neural machine translation for extremely low resource languages"
    - "large scale distributed neural network training through online distillation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "double path networks for sequence to sequence learning"
    - "knowledge distillation in generations more tolerant teachers educate better students"

Connected through hyperedge HE-4 (induced by source paper "hierarchically structured meta learning"):
  with members:
    - "on first order meta learning algorithms"
    - "toward multimodal model agnostic meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "bayesian model agnostic meta learning"
    - "hierarchical graph representation learning with differentiable pooling"
    - "transferring knowledge across learning processes"
    - "optimization landscape and expressivity of deep cnns"

Connected through hyperedge HE-5 (induced by source paper "a study of multilingual neural machine translation"):
  with members:
    - "unsupervised pivot translation for distant languages"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "multilingual neural machine translation with language clustering"
    - "language graph distillation for low resource machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "universal neural machine translation for extremely low resource languages"
    - "parameter sharing methods for multilingual self attentional translation models"

Connected through hyperedge HE-6 (induced by source paper "cognitive knowledge graph reasoning for one shot relational learning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "relational inductive biases deep learning and graph networks"
    - "variational knowledge graph reasoning"
    - "one shot relational learning for knowledge graphs"
    - "m walk learning to walk over graphs using monte carlo tree search"

Connected through hyperedge HE-7 (induced by source paper "domain adaptive dialog generation via meta learning"):
  with members:
    - "few shot generalization across dialogue tasks"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "sentiment adaptive end to end dialog systems"
    - "adversarial domain adaptation for variational neural language generation in dialogue systems"
    - "prototypical networks for few shot learning"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"

Connected through hyperedge HE-8 (induced by source paper "improved zero shot neural machine translation via ignoring spurious correlations"):
  with members:
    - "cross lingual language model pretraining"
    - "the missing ingredient in zero shot neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "zero resource neural machine translation with multi agent communication game"
    - "contextual parameter generation for universal neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "zero shot dual machine translation"
    - "a teacher student framework for zero resource neural machine translation"

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)