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: "target conditioned sampling optimizing data selection for multilingual neural machine translation"
  Abstract: To improve low-resource Neural Machine Translation (NMT) with multilingual corpora, training on the most related high-resource language only is often more effective than using all data available (Neubig and Hu, 2018). However, it is possible that an intelligent data selection strategy can further improve low-resource NMT with data from other auxiliary languages. In this paper, we seek to construct a sampling distribution over all multilingual data, so that it minimizes the training loss of the low-resource language. Based on this formulation, we propose an efficient algorithm, Target Conditioned Sampling (TCS), which first samples a target sentence, and then conditionally samples its source sentence. Experiments show that TCS brings significant gains of up to 2 BLEU on three of four languages we test, with minimal training overhead.

Connected through hyperedge HE-1 (induced by source paper "pre training via leveraging assisting languages and data selection for neural machine translation"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "addressing word order divergence in multilingual neural machine translation for extremely low resource languages"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transfer learning for low resource neural machine translation"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-2 (induced by source paper "optimizing data usage via differentiable rewards"):
  with members:
    - "revisiting low resource neural machine translation a case study"
    - "self supervised generalisation with meta auxiliary learning"
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "do better imagenet models transfer better"
    - "reinforced co training"

Connected through hyperedge HE-3 (induced by source paper "coursera corpus mining and multistage fine tuning for improving lectures translation"):
  with members:
    - "exploiting sentential context for neural machine translation"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "exploiting out of domain parallel data through multilingual transfer learning for low resource neural machine translation"
    - "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion"
    - "attention is all you need"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "neural machine translation"
    - "six challenges for 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)