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: "dependency parsing as head selection"
  Abstract: Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call \textsc{DeNSe} (as shorthand for {\bf De}pendency {\bf N}eural {\bf Se}lection) produces a distribution over possible heads for each word using features obtained from a bidirectional recurrent neural network. Without enforcing structural constraints during training, \textsc{DeNSe} generates (at inference time) trees for the overwhelming majority of sentences, while non-tree outputs can be adjusted with a maximum spanning tree algorithm. We evaluate \textsc{DeNSe} on four languages (English, Chinese, Czech, and German) with varying degrees of non-projectivity. Despite the simplicity of the approach, our parsers are on par with the state of the art.

Connected through hyperedge HE-1 (induced by source paper "syntactically look ahead attention network for sentence compression"):
  with members:
    - "a unified linear time framework for sentence level discourse parsing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural machine translation with source side latent graph parsing"
    - "improving sentence compression by learning to predict gaze"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "multi task semantic dependency parsing with policy gradient for learning easy first strategies"):
  with members:
    - "backpropagating through structured argmax using a spigot"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "learning joint semantic parsers from disjoint data"
    - "policy gradient as a proxy for dynamic oracles in constituency parsing"
    - "simpler but more accurate semantic dependency parsing"
    - "a transition based directed acyclic graph parser for ucca"
    - "deep multitask learning for semantic dependency parsing"
    - "recurrent neural network grammars"

Connected through hyperedge HE-4 (induced by source paper "source dependency aware transformer with supervised self attention"):
  with members:
    - "attention is all you need"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "convolutional sequence to sequence learning"
    - "tree to sequence attentional neural machine translation"
    - "linguistic input features improve neural machine translation"
    - "a convolutional encoder model for neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-5 (induced by source paper "viable dependency parsing as sequence labeling"):
  with members:
    - "constituent parsing as sequence labeling"
    - "scheduled multi task learning from syntax to translation"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "sequence to sequence learning as beam search optimization"
    - "bidirectional lstm crf models for sequence tagging"
    - "transition based dependency parsing with stack long short term memory"
    - "grammar as a foreign language"

Connected through hyperedge HE-6 (induced by source paper "a neural joint model for vietnamese word segmentation pos tagging and dependency parsing"):
  with members:
    - "error analysis for vietnamese dependency parsing"
    - "vietnamese transition based dependency parsing with supertag features"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "vncorenlp a vietnamese natural language processing toolkit"
    - "lstm easy first dependency parsing with pre trained word embeddings and character level word embeddings in vietnamese"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a fast and accurate vietnamese word segmenter"

Connected through hyperedge HE-7 (induced by source paper "joint entity recognition and relation extraction as a multi head selection problem"):
  with members:
    - "an attentive neural architecture for joint segmentation and parsing and its application to real estate ads"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "tensorflow a system for large scale machine learning"
    - "combining recurrent and convolutional neural networks for relation classification"
    - "bidirectional lstm crf models for sequence tagging"
    - "classifying relations by ranking with convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "an improved neural network model for joint"):
  with members:
    - "from pos tagging to dependency parsing for biomedical event extraction"
    - "neural probabilistic model for non projective mst parsing"
    - "a novel neural network model for joint pos tagging and graph based dependency parsing"
    - "globally normalized transition based neural networks"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"

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)