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: "neural versus phrase based machine translation quality a case study"
  Abstract: Within the field of Statistical Machine Translation (SMT), the neural approach (NMT) has recently emerged as the first technology able to challenge the long-standing dominance of phrase-based approaches (PBMT). In particular, at the IWSLT 2015 evaluation campaign, NMT outperformed well established state-of-the-art PBMT systems on English-German, a language pair known to be particularly hard because of morphology and syntactic differences. To understand in what respects NMT provides better translation quality than PBMT, we perform a detailed analysis of neural versus phrase-based SMT outputs, leveraging high quality post-edits performed by professional translators on the IWSLT data. For the first time, our analysis provides useful insights on what linguistic phenomena are best modeled by neural models -- such as the reordering of verbs -- while pointing out other aspects that remain to be improved.

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 "context in neural machine translation a review of models and evaluations"):
  with members:
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "assessing crosslingual discourse relations in machine translation"
    - "pronoun translation in english french machine translation an analysis of error types"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "marian fast neural machine translation in c"
    - "automatic reference based evaluation of pronoun translation misses the point"
    - "context aware neural machine translation learns anaphora resolution"
    - "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "learning neural sequence to sequence models from weak feedback with bipolar ramp loss"):
  with members:
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "semi supervised qa with generative domain adaptive nets"
    - "coupling distributed and symbolic execution for natural language queries"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"

Connected through hyperedge HE-4 (induced by source paper "lost in translation loss and decay of linguistic richness in machine translation"):
  with members:
    - "assessing gender bias in machine translation a case study with google translate"
    - "getting gender right in neural machine translation"
    - "attention is all you need"
    - "massive exploration of neural machine translation architectures"
    - "improving neural machine translation models with monolingual data"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "learning cross lingual phonological and orthagraphic adaptations a case study in improving neural machine translation between low resource languages"):
  with members:
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"):
  with members:
    - "self attention with relative position representations"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"
    - "stronger baselines for trustable results in neural machine translation"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"

Connected through hyperedge HE-7 (induced by source paper "deep neural machine translation with weakly recurrent units"):
  with members:
    - "the best of both worlds combining recent advances in neural machine translation"
    - "simple recurrent units for highly parallelizable recurrence"
    - "deriving neural architectures from sequence and graph kernels"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "non autoregressive neural machine translation"
    - "convolutional sequence to sequence learning"
    - "deep architectures for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "analyzing uncertainty in neural machine translation"):
  with members:
    - "a challenge set approach to evaluating machine translation"
    - "on calibration of modern neural networks"
    - "analyzing neural mt search and model performance"
    - "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"
    - "variational 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)