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: "handling homographs in neural machine translation"
  Abstract: Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the context. However, with the advent of neural machine translation (NMT) systems, which can theoretically take into account global sentential context, one may hypothesize that this problem has been alleviated. In this paper, we first provide empirical evidence that existing NMT systems in fact still have significant problems in properly translating ambiguous words. We then proceed to describe methods, inspired by the word sense disambiguation literature, that model the context of the input word with context-aware word embeddings that help to differentiate the word sense be- fore feeding it into the encoder. Experiments on three language pairs demonstrate that such models improve the performance of NMT systems both in terms of BLEU score and in the accuracy of translating homographs.

Connected through hyperedge HE-1 (induced by source paper "on the linguistic representational power of neural machine translation models"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "linguistic knowledge and transferability of contextual representations"
    - "the missing ingredient in zero shot neural machine translation"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "bpe and charcnns for translation of morphology a cross lingual comparison and analysis"

Connected through hyperedge HE-2 (induced by source paper "competence based curriculum learning for neural machine translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "training tips for the transformer model"
    - "contextual parameter generation for universal neural machine translation"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "curriculum learning and minibatch bucketing in neural machine translation"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "one homonym per translation"):
  with members:
    - "towards a seamless integration of word senses into downstream nlp applications"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "one sense per collocation and genre topic variations"

Connected through hyperedge HE-4 (induced by source paper "from word to sense embeddings a survey on vector representations of meaning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "seven augmenting word embeddings with unsupervised relation vectors"
    - "incorporating glosses into neural word sense disambiguation"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "knowledge based word sense disambiguation using topic models"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-5 (induced by source paper "on the evaluation of semantic phenomena in neural machine translation using natural language inference"):
  with members:
    - "hypothesis only baselines in natural language inference"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "analogs of linguistic structure in deep representations"
    - "attention is all you need"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "evaluating discourse phenomena in neural machine translation"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"

Connected through hyperedge HE-6 (induced by source paper "integrating weakly supervised word sense disambiguation into neural machine translation"):
  with members:
    - "attention is all you need"
    - "self attentive residual decoder for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "context dependent word representation for neural machine translation"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "effective approaches to attention based neural machine translation"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "do multi sense embeddings improve natural language understanding"

Connected through hyperedge HE-7 (induced by source paper "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"):
  with members:
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "sockeye a toolkit for neural machine translation"
    - "what does attention in neural machine translation pay attention to"
    - "six challenges for neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "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)