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: "improving sentence compression by learning to predict gaze"
  Abstract: We show how eye-tracking corpora can be used to improve sentence compression models, presenting a novel multi-task learning algorithm based on multi-layer LSTMs. We obtain performance competitive with or better than state-of-the-art approaches.

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"
    - "dependency parsing as head selection"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "query focused sentence compression in linear time"):
  with members:
    - "non entailed subsequences as a challenge for natural language inference"
    - "bottom up abstractive summarization"
    - "allennlp a deep semantic natural language processing platform"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "monte carlo syntax marginals for exploring and using dependency parses"
    - "sentence level fluency evaluation references help but can be spared"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "controlling output length in neural encoder decoders"

Connected through hyperedge HE-3 (induced by source paper "advancing nlp with cognitive language processing signals"):
  with members:
    - "entity recognition at first sight improving ner with eye movement information"
    - "finding syntax in human encephalography with beam search"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "replicability analysis for natural language processing testing significance with multiple datasets"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "leveraging cognitive features for sentiment analysis"
    - "what to do about non standard or non canonical language in nlp"

Connected through hyperedge HE-4 (induced by source paper "zuco 2 0 a dataset of physiological recordings during natural reading and annotation"):
  with members:
    - "interpreting and improving natural language processing in machines with natural language processing in the brain"
    - "cognival a framework for cognitive word embedding evaluation"
    - "finding syntax in human encephalography with beam search"
    - "harnessing cognitive features for sarcasm detection"

Connected through hyperedge HE-5 (induced by source paper "multi task learning for sequence tagging an empirical study"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "scheduled multi task learning from syntax to translation"
    - "learning to parse and translate improves neural machine translation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "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"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "multitask parsing across semantic representations"):
  with members:
    - "learning joint semantic parsers from disjoint data"
    - "a transition based directed acyclic graph parser for ucca"
    - "neural amr sequence to sequence models for parsing and generation"
    - "improving generalization performance by switching from adam to sgd"
    - "robust incremental neural semantic graph parsing"
    - "transfer learning for neural semantic parsing"
    - "deep multitask learning for semantic dependency parsing"
    - "exploiting multi typed treebanks for parsing with deep multi task learning"

Connected through hyperedge HE-7 (induced by source paper "multi task identification of entities relations and coreference for scientific knowledge graph construction"):
  with members:
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "construction of the literature graph in semantic scholar"
    - "cross sentence n ary relation extraction with graph lstms"
    - "end to end neural coreference resolution"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "semi supervised multitask learning for sequence labeling"

Connected through hyperedge HE-8 (induced by source paper "neural davidsonian semantic proto role labeling"):
  with members:
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "deep contextualized word representations"
    - "neural models of factuality"
    - "universal language model fine tuning for text classification"
    - "opennmt open source toolkit for neural machine translation"
    - "learned in translation contextualized word vectors"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "a joint many task model growing a neural network for multiple nlp tasks"

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)