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: "optimizing segmentation granularity for neural machine translation"
  Abstract: In neural machine translation (NMT), it is has become standard to translate using subword units to allow for an open vocabulary and improve accuracy on infrequent words. Byte-pair encoding (BPE) and its variants are the predominant approach to generating these subwords, as they are unsupervised, resource-free, and empirically effective. However, the granularity of these subword units is a hyperparameter to be tuned for each language and task, using methods such as grid search. Tuning may be done inexhaustively or skipped entirely due to resource constraints, leading to sub-optimal performance. In this paper, we propose a method to automatically tune this parameter using only one training pass. We incrementally introduce new vocabulary online based on the held-out validation loss, beginning with smaller, general subwords and adding larger, more specific units over the course of training. Our method matches the results found with grid search, optimizing segmentation granularity without any additional training time. We also show benefits in training efficiency and performance improvements for rare words due to the way embeddings for larger units are incrementally constructed by combining those from smaller units.

Connected through hyperedge HE-1 (induced by source paper "exploring phoneme level speech representations for end to end speech translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "tied multitask learning for neural speech translation"
    - "low resource speech to text translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "domain robust feature extraction for rapid low resource asr development"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "self attentional acoustic models"

Connected through hyperedge HE-2 (induced by source paper "multitask learning for different subword segmentations in neural machine translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "attention is all you need"
    - "exploiting linguistic resources for neural machine translation using multi task learning"

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)