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: "when is multitask learning effective semantic sequence prediction under varying data conditions"
  Abstract: Multitask learning has been applied successfully to a range of tasks, mostly morphosyntactic. However, little is known on when MTL works and whether there are data characteristics that help to determine its success. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary tasks, amongst which a novel setup, and correlate their impact to data-dependent conditions. Our results show that MTL is not always effective, significant improvements are obtained only for 1 out of 5 tasks. When successful, auxiliary tasks with compact and more uniform label distributions are preferable.

Connected through hyperedge HE-1 (induced by source paper "parsing thai social data a new challenge for thai nlp"):
  with members:
    - "a simple lstm model for transition based dependency parsing"
    - "semantic tagging with deep residual networks"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"
    - "a universal part of speech tagset"

Connected through hyperedge HE-2 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "deep learning for sentiment analysis a survey"
    - "code2vec learning distributed representations of code"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"
    - "generalizing word embeddings using bag of subwords"
    - "dynamic meta embeddings for improved sentence representations"

Connected through hyperedge HE-3 (induced by source paper "multi task learning for argumentation mining"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "lstm a search space odyssey"
    - "neural end to end learning for computational argumentation mining"
    - "multi task learning for mental health using social media text"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "attention strategies for multi source sequence to sequence learning"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "cross lingual and cross domain discourse segmentation of entire documents"

Connected through hyperedge HE-4 (induced by source paper "bam born again multi task networks for natural language understanding"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "multi task deep neural networks for natural language understanding"
    - "unifying question answering text classification and regression via span extraction"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "unifying question answering and text classification via span extraction"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "the natural language decathlon multitask learning as question answering"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"

Connected through hyperedge HE-5 (induced by source paper "task selection policies for multitask learning"):
  with members:
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "evaluating the supervised and zero shot performance of multi lingual translation models"
    - "autosem automatic task selection and mixing in multi task learning"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "off the shelf unsupervised nmt"
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "deconstructing and reconstructing word embedding algorithms"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "dynamic meta embeddings for improved sentence representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi task learning for sequence tagging an empirical study"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "reconstruction of word embeddings from sub word parameters"
    - "compressing word embeddings via deep compositional code learning"

Connected through hyperedge HE-7 (induced by source paper "gated task interaction framework for multi task sequence tagging"):
  with members:
    - "aspect based sentiment analysis with gated convolutional networks"
    - "linguistically informed self attention for semantic role labeling"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"

Connected through hyperedge HE-8 (induced by source paper "multi task self supervised learning for disfluency detection"):
  with members:
    - "giving attention to the unexpected using prosody innovations in disfluency detection"
    - "robust cross domain disfluency detection with pattern match networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "disfluency detection using auto correlational neural networks"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

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)