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: "bidirectional tree structured lstm with head lexicalization"
  Abstract: Sequential LSTM has been extended to model tree structures, giving competitive results for a number of tasks. Existing methods model constituent trees by bottom-up combinations of constituent nodes, making direct use of input word information only for leaf nodes. This is different from sequential LSTMs, which contain reference to input words for each node. In this paper, we propose a method for automatic head-lexicalization for tree-structure LSTMs, propagating head words from leaf nodes to every constituent node. In addition, enabled by head lexicalization, we build a tree LSTM in the top-down direction, which corresponds to bidirectional sequential LSTM structurally. Experiments show that both extensions give better representations of tree structures. Our final model gives the best results on the Standford Sentiment Treebank and highly competitive results on the TREC question type classification task.

Connected through hyperedge HE-1 (induced by source paper "syntax enhanced neural machine translation with syntax aware word representations"):
  with members:
    - "towards robust neural machine translation"
    - "deep contextualized word representations"
    - "structural attention neural networks for improved sentiment analysis"
    - "towards bidirectional hierarchical representations for attention based neural machine translation"
    - "attention is all you need"
    - "neural machine translation with source side latent graph parsing"
    - "chunk based bi scale decoder for neural machine translation"
    - "stronger baselines for trustable results in neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "neural language priors"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "improving tree lstm with tree attention"
    - "no training required exploring random encoders for sentence classification"
    - "universal transformers"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "universal sentence encoder"

Connected through hyperedge HE-3 (induced by source paper "attention driven tree structured convolutional lstm for high dimensional data understanding"):
  with members:
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "residual attention network for image classification"
    - "unsupervised learning for physical interaction through video prediction"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (induced by source paper "ipod corpus of 190 000 industrial occupations"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "roberta a robustly optimized bert pretraining approach"
    - "chinese ner using lattice lstm"
    - "enhancing person job fit for talent recruitment an ability aware neural network approach"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "multi granular text encoding for self explaining categorization"):
  with members:
    - "explainable prediction of medical codes from clinical text"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "a structured self attentive sentence embedding"
    - "rationalizing neural predictions"
    - "visualizing and understanding neural models in nlp"
    - "self adaptive hierarchical sentence model"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-6 (induced by source paper "a better way to attend attention with trees for video question answering"):
  with members:
    - "tree structured reinforcement learning for sequential object localization"
    - "tree to sequence attentional neural machine translation"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "image captioning with semantic attention"
    - "a focused dynamic attention model for visual question answering"
    - "enhanced lstm for natural language inference"
    - "video fill in the blank with merging lstms"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "two local models for neural constituent parsing"):
  with members:
    - "in order transition based constituent parsing"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "a minimal span based neural constituency parser"
    - "neural probabilistic model for non projective mst parsing"
    - "improving neural parsing by disentangling model combination and reranking effects"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"

Connected through hyperedge HE-8 (induced by source paper "improving aspect term extraction with bidirectional dependency tree representation"):
  with members:
    - "lifelong learning crf for supervised aspect extraction"
    - "unsupervised aspect term extraction with b lstm crf using automatically labelled datasets"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "multi task memory networks for category specific aspect and opinion terms co extraction"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)