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: "structured training for neural network transition based parsing"
  Abstract: We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a large number of automatically parsed sentences. Given this fixed network representation, we learn a final layer using the structured perceptron with beam-search decoding. On the Penn Treebank, our parser reaches 94.26% unlabeled and 92.41% labeled attachment accuracy, which to our knowledge is the best accuracy on Stanford Dependencies to date. We also provide in-depth ablative analysis to determine which aspects of our model provide the largest gains in accuracy.

Connected through hyperedge HE-1 (induced by source paper "concurrent parsing of constituency and dependency"):
  with members:
    - "sequence labeling parsing by learning across representations"
    - "what s going on in neural constituency parsers an analysis"
    - "two local models for neural constituent parsing"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "constituency parsing with a self attentive encoder"

Connected through hyperedge HE-2 (induced by source paper "deep contextualized word embeddings in transition based and graph based dependency parsing a tale of two parsers revisited"):
  with members:
    - "assessing bert s syntactic abilities"
    - "universal dependency parsing from scratch"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the non utility of structural features in bilstm based dependency parsers"
    - "75 languages 1 model parsing universal dependencies universally"
    - "linguistic knowledge and transferability of contextual representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"

Connected through hyperedge HE-3 (induced by source paper "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "understanding back translation at scale"
    - "neural machine translation with gumbel greedy decoding"
    - "attention is all you need"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"

Connected through hyperedge HE-4 (induced by source paper "graph to graph transformer for transition based dependency parsing"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "assessing bert s syntactic abilities"
    - "recurrent graph syntax encoder for neural machine translation"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"
    - "cross sentence n ary relation extraction with graph lstms"

Connected through hyperedge HE-5 (induced by source paper "structured minimally supervised learning for neural relation extraction"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "combining distant and direct supervision for neural relation extraction"
    - "event representations with tensor based compositions"
    - "weakly supervised relation extraction by pattern enhanced embedding learning"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "globally normalized transition based neural networks"
    - "neural architectures for named entity recognition"
    - "noise mitigation for neural entity typing and relation extraction"

Connected through hyperedge HE-6 (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-7 (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"

Connected through hyperedge HE-8 (induced by source paper "sentence encoding with tree constrained relation networks"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "structured attention networks"
    - "learning structured text representations"
    - "attention is all you need"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "neural probabilistic model for non projective mst parsing"
    - "graph convolutional encoders for syntax aware 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)