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: "in order transition based constituent parsing"
  Abstract: Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation tree, where bottom-up strategies and top-down strategies take post-order and pre-order traversal over trees, respectively. Bottom-up parsers benefit from rich features from readily built partial parses, but lack lookahead guidance in the parsing process; top-down parsers benefit from non-local guidance for local decisions, but rely on a strong encoder over the input to predict a constituent hierarchy before its construction.To mitigate both issues, we propose a novel parsing system based on in-order traversal over syntactic trees, designing a set of transition actions to find a compromise between bottom-up constituent information and top-down lookahead information. Based on stack-LSTM, our psycholinguistically motivated constituent parsing system achieves 91.8 F1 on WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised reranking and 94.2 F1 with semi-supervised reranking, which are the best results on the WSJ benchmark.

Connected through hyperedge HE-1 (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-2 (induced by source paper "leveraging dependency forest for neural medical relation extraction"):
  with members:
    - "graph based neural sentence ordering"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "a silver standard corpus of human phenotype gene relations"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "semantic neural machine translation using amr"
    - "attention guided graph convolutional networks for relation extraction"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"

Connected through hyperedge HE-3 (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-4 (induced by source paper "better faster stronger sequence tagging constituent parsers"):
  with members:
    - "multilingual constituency parsing with self attention and pre training"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "deep contextualized word representations"
    - "constituent parsing as sequence labeling"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ncrf an open source neural sequence labeling toolkit"
    - "constituency parsing with a self attentive encoder"

Connected through hyperedge HE-5 (induced by source paper "faster shift reduce constituent parsing with a non binary bottom up strategy"):
  with members:
    - "what s going on in neural constituency parsers an analysis"
    - "syntactic scaffolds for semantic structures"
    - "a tree based decoder for neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "efficient dependency guided named entity recognition"
    - "a minimal span based neural constituency parser"
    - "towards string to tree neural machine translation"
    - "modeling source syntax for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "cross domain generalization of neural constituency parsers"):
  with members:
    - "finding syntax in human encephalography with beam search"
    - "extending a parser to distant domains using a few dozen partially annotated examples"
    - "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"
    - "a minimal span based neural constituency parser"

Connected through hyperedge HE-7 (induced by source paper "unlexicalized transition based discontinuous constituency parsing"):
  with members:
    - "what s going on in neural constituency parsers an analysis"
    - "constituency parsing with a self attentive encoder"
    - "a minimal span based neural constituency parser"
    - "optimizing spectral learning for parsing"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "recurrent neural network grammars"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-8 (induced by source paper "tetra tagging word synchronous parsing with linear time inference"):
  with members:
    - "better faster stronger sequence tagging constituent parsers"
    - "multilingual constituency parsing with self attention and pre training"
    - "constituent parsing as sequence labeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a minimal span based neural constituency parser"
    - "recurrent neural network grammars"
    - "global neural ccg parsing with optimality guarantees"
    - "neural crf parsing"

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)