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: "keystroke dynamics as signal for shallow syntactic parsing"
  Abstract: Keystroke dynamics have been extensively used in psycholinguistic and writing research to gain insights into cognitive processing. But do keystroke logs contain actual signal that can be used to learn better natural language processing models? #R##N#We postulate that keystroke dynamics contain information about syntactic structure that can inform shallow syntactic parsing. To test this hypothesis, we explore labels derived from keystroke logs as auxiliary task in a multi-task bidirectional Long Short-Term Memory (bi-LSTM). Our results show promising results on two shallow syntactic parsing tasks, chunking and CCG supertagging. Our model is simple, has the advantage that data can come from distinct sources, and produces models that are significantly better than models trained on the text annotations alone.

Connected through hyperedge HE-1 (induced by source paper "improving sentiment analysis with multi task learning of negation"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "a multi sentiment resource enhanced attention network for sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transfer and multi task learning for noun noun compound interpretation"
    - "universal language model fine tuning for text classification"
    - "self attention a better building block for sentiment analysis neural network classifiers"

Connected through hyperedge HE-2 (induced by source paper "bootstrapping generators from noisy data"):
  with members:
    - "handling divergent reference texts when evaluating table to text generation"
    - "sentence simplification with deep reinforcement learning"
    - "what does attention in neural machine translation pay attention to"
    - "learning to generate one sentence biographies from wikidata"
    - "order planning neural text generation from structured data"
    - "challenges in data to document generation"
    - "table to text generation by structure aware seq2seq learning"
    - "semantic tagging with deep residual networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "imagination improves multimodal translation"):
  with members:
    - "doubly attentive decoder for multi modal neural machine translation"
    - "neural machine translation with latent semantic of image and text"
    - "multi task video captioning with video and entailment generation"
    - "a correlational encoder decoder architecture for pivot based sequence generation"
    - "representation of linguistic form and function in recurrent neural networks"
    - "zero resource machine translation by multimodal encoder decoder network with multimedia pivot"
    - "multimodal pivots for image caption translation"
    - "variational neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "multitask learning for fine grained twitter sentiment analysis"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "twise at semeval 2016 task 4 twitter sentiment classification"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "recurrent neural network for text classification with multi task learning"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "bidirectional lstm crf models for sequence tagging"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-6 (induced by source paper "identifying beneficial task relations for multi task learning in deep neural networks"):
  with members:
    - "how transferable are neural networks in nlp applications"
    - "improving sentence compression by learning to predict gaze"
    - "improving historical spelling normalization with bi directional lstms and multi task learning"
    - "multi task sequence to sequence learning"
    - "a universal part of speech tagset"
    - "a model of inductive bias learning"

Connected through hyperedge HE-7 (induced by source paper "when is multitask learning effective semantic sequence prediction under varying data conditions"):
  with members:
    - "semantic tagging with deep residual networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "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"
    - "neural architectures for named entity recognition"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "bidirectional lstm crf models for sequence tagging"
    - "transition based dependency parsing with stack long short term memory"

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)