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: "beyond bleu training neural machine translation with semantic similarity"
  Abstract: While most neural machine translation (NMT) systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can substantially improve final translation accuracy. However, training with BLEU has some limitations: it doesn't assign partial credit, it has a limited range of output values, and it can penalize semantically correct hypotheses if they differ lexically from the reference. In this paper, we introduce an alternative reward function for optimizing NMT systems that is based on recent work in semantic similarity. We evaluate on four disparate languages translated to English, and find that training with our proposed metric results in better translations as evaluated by BLEU, semantic similarity, and human evaluation, and also that the optimization procedure converges faster. Analysis suggests that this is because the proposed metric is more conducive to optimization, assigning partial credit and providing more diversity in scores than BLEU.

Connected through hyperedge HE-1 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "the curious case of neural text degeneration"
    - "bertscore evaluating text generation with bert"
    - "on the use of bert for neural machine translation"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "generalization in generation a closer look at exposure bias"

Connected through hyperedge HE-2 (induced by source paper "bertscore evaluating text generation with bert"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "fine tune bert for extractive summarization"
    - "alternative weighting schemes for elmo embeddings"
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "simple applications of bert for ad hoc document retrieval"
    - "unifying human and statistical evaluation for natural language generation"
    - "scibert a pretrained language model for scientific text"

Connected through hyperedge HE-3 (induced by source paper "a bilingual generative transformer for semantic sentence embedding"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "simple and effective paraphrastic similarity from parallel translations"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "cost sensitive training for autoregressive models"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "on the weaknesses of reinforcement learning for neural machine translation"
    - "bridging the gap between training and inference for neural machine translation"
    - "non monotonic sequential text generation"
    - "on nmt search errors and model errors cat got your tongue"
    - "optimal completion distillation for sequence learning"
    - "loss functions for multiset prediction"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "modeling fluency and faithfulness for diverse neural machine translation"):
  with members:
    - "bridging the gap between training and inference for neural machine translation"
    - "synchronous bidirectional inference for neural sequence generation"
    - "achieving human parity on automatic chinese to english news translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "a study of reinforcement learning for neural machine translation"
    - "greedy search with probabilistic n gram matching for neural machine translation"
    - "bag of words as target for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning with distributional semantic rewards for abstractive summarization"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diversity driven attention model for query based abstractive summarization"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "neural combinatorial optimization with reinforcement 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)