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: "distilling an ensemble of greedy dependency parsers into one mst parser"
  Abstract: We introduce two first-order graph-based dependency parsers achieving a new state of the art. The first is a consensus parser built from an ensemble of independently trained greedy LSTM transition-based parsers with different random initializations. We cast this approach as minimum Bayes risk decoding (under the Hamming cost) and argue that weaker consensus within the ensemble is a useful signal of difficulty or ambiguity. The second parser is a "distillation" of the ensemble into a single model. We train the distillation parser using a structured hinge loss objective with a novel cost that incorporates ensemble uncertainty estimates for each possible attachment, thereby avoiding the intractable cross-entropy computations required by applying standard distillation objectives to problems with structured outputs. The first-order distillation parser matches or surpasses the state of the art on English, Chinese, and German.

Connected through hyperedge HE-1 (induced by source paper "bam born again multi task networks for natural language understanding"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "multi task deep neural networks for natural language understanding"
    - "unifying question answering text classification and regression via span extraction"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "unifying question answering and text classification via span extraction"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "the natural language decathlon multitask learning as question answering"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"

Connected through hyperedge HE-2 (induced by source paper "head driven phrase structure grammar parsing on penn treebank"):
  with members:
    - "lattice based transformer encoder for neural machine translation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what s going on in neural constituency parsers an analysis"
    - "two local models for neural constituent parsing"
    - "exploring recombination for efficient decoding of neural machine translation"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "model compression with two stage multi teacher knowledge distillation for web question answering system"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "bam born again multi task networks for natural language understanding"
    - "model compression via distillation and quantization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "target language aware constrained inference for cross lingual dependency parsing"):
  with members:
    - "how multilingual is multilingual bert"
    - "low resource syntactic transfer with unsupervised source reordering"
    - "dependency grammar induction with a neural variational transition based parser"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "attention is all you need"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "word translation without parallel data"

Connected through hyperedge HE-5 (induced by source paper "massively multilingual transfer for ner"):
  with members:
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "learning how to active learn a deep reinforcement learning approach"
    - "snorkel rapid training data creation with weak supervision"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "left to right dependency parsing with pointer networks"):
  with members:
    - "arc swift a novel transition system for dependency parsing"
    - "training with exploration improves a greedy stack lstm parser"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "bi directional attention with agreement for dependency parsing"
    - "transition based dependency parsing with stack long short term memory"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "scheduled multi task learning from syntax to translation"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "one model to learn them all"
    - "what do neural machine translation models learn about morphology"
    - "syntax aware neural machine translation using ccg"
    - "recurrent neural network grammars"

Connected through hyperedge HE-8 (induced by source paper "stack pointer networks for dependency parsing"):
  with members:
    - "neural probabilistic model for non projective mst parsing"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "unsupervised ranking model for entity coreference resolution"
    - "globally normalized transition based neural networks"
    - "training with exploration improves a greedy stack lstm parser"
    - "dropout with expectation linear regularization"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

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)