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: "multi task learning for sequence tagging an empirical study"
  Abstract: We study three general multi-task learning (MTL) approaches on 11 sequence tagging tasks. Our extensive empirical results show that in about 50% of the cases, jointly learning all 11 tasks improves upon either independent or pairwise learning of the tasks. We also show that pairwise MTL can inform us what tasks can benefit others or what tasks can be benefited if they are learned jointly. In particular, we identify tasks that can always benefit others as well as tasks that can always be harmed by others. Interestingly, one of our MTL approaches yields embeddings of the tasks that reveal the natural clustering of semantic and syntactic tasks. Our inquiries have opened the doors to further utilization of MTL in NLP.

Connected through hyperedge HE-1 (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-2 (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"
    - "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"
    - "efficient softmax approximation for gpus"

Connected through hyperedge HE-3 (induced by source paper "learning to infer entities properties and their relations from clinical conversations"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "extracting symptoms and their status from clinical conversations"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "neural relation extraction within and across sentence boundaries"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi labeled relation extraction with attentive capsule network"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "multi task learning with contextualized word representations for extented named entity recognition"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "neural adaptation layers for cross domain named entity recognition"
    - "end to end recurrent neural network models for vietnamese named entity recognition word level vs character level"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "extracting symptoms and their status from clinical conversations"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised sequence tagging with bidirectional language models"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "bidirectional attention flow for machine comprehension"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-6 (induced by source paper "how to best use syntax in semantic role labelling"):
  with members:
    - "syntax aware neural semantic role labeling"
    - "syntactic scaffolds for semantic structures"
    - "a span selection model for semantic role labeling"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "constituent parsing as sequence labeling"
    - "evaluating the utility of hand crafted features in sequence labelling"
    - "constituency parsing with a self attentive encoder"

Connected through hyperedge HE-7 (induced by source paper "factored latent dynamic conditional random fields for single and multi label sequence modeling"):
  with members:
    - "context model for pedestrian intention prediction using factored latent dynamic conditional random fields"
    - "embedded state latent conditional random fields for sequence labeling"
    - "multi attention recurrent network for human communication comprehension"
    - "video based sign language recognition without temporal segmentation"
    - "lstm a search space odyssey"
    - "joint learning of correlated sequence labelling tasks using bidirectional recurrent neural networks"
    - "hcp a flexible cnn framework for multi label image classification"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-8 (induced by source paper "a framework for identifying group behavior of wild animals"):
  with members:
    - "xgboost a scalable tree boosting system"

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)