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: "transfer learning for sequence tagging with hierarchical recurrent networks"
  Abstract: Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their generality, as excellent performance can be achieved with a unified architecture and without task-specific feature engineering. However, it is unclear if such systems can be used for tasks without large amounts of training data. In this paper we explore the problem of transfer learning for neural sequence taggers, where a source task with plentiful annotations (e.g., POS tagging on Penn Treebank) is used to improve performance on a target task with fewer available annotations (e.g., POS tagging for microblogs). We examine the effects of transfer learning for deep hierarchical recurrent networks across domains, applications, and languages, and show that significant improvement can often be obtained. These improvements lead to improvements over the current state-of-the-art on several well-studied tasks.

Connected through hyperedge HE-1 (induced by source paper "zero resource cross domain named entity recognition"):
  with members:
    - "do we need word order information for cross lingual sequence labeling"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"
    - "robust zero shot cross domain slot filling with example values"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "neural adaptation layers for cross domain named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "schema guided dialogue state tracking task at dstc8"):
  with members:
    - "fine tuning bert for schema guided zero shot dialogue state tracking"
    - "goal oriented multi task bert based dialogue state tracker"
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "robust zero shot cross domain slot filling with example values"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "transferable multi domain state generator for task oriented dialogue systems"

Connected through hyperedge HE-3 (induced by source paper "multi task learning based neural bridging reference resolution"):
  with members:
    - "bam born again multi task networks for natural language understanding"
    - "enhanced word representations for bridging anaphora resolution"
    - "a deterministic algorithm for bridging anaphora resolution"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scheduled multi task learning from syntax to translation"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-4 (induced by source paper "cross lingual transfer learning for question answering"):
  with members:
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual language model pretraining"
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "multilingual extractive reading comprehension by runtime machine translation"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "drcd a chinese machine reading comprehension dataset"

Connected through hyperedge HE-5 (induced by source paper "hierarchical contextualized representation for named entity recognition"):
  with members:
    - "semantics aware bert for language understanding"
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "syntax aware multilingual semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "gcdt a global context enhanced deep transition architecture for sequence labeling"
    - "dual co matching network for multi choice reading comprehension"
    - "robust lexical features for improved neural network named entity recognition"
    - "contextualized non local neural networks for sequence learning"

Connected through hyperedge HE-6 (induced by source paper "gcdt a global context enhanced deep transition architecture for sequence labeling"):
  with members:
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "neural reranking for named entity recognition"
    - "attention is all you need"
    - "neural models for sequence chunking"
    - "semi supervised multitask learning for sequence labeling"

Connected through hyperedge HE-7 (induced by source paper "learning to selectively transfer reinforced transfer learning for deep text matching"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "reinforced co training"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "learning sampling policies for domain adaptation"
    - "reinforcement learning for relation classification from noisy data"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "learning how to active learn a deep reinforcement learning approach"

Connected through hyperedge HE-8 (induced by source paper "grn gated relation network to enhance convolutional neural network for named entity recognition"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "hybrid semi markov crf for neural sequence labeling"
    - "deep contextualized word representations"
    - "deep active learning for named entity recognition"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "semi supervised multitask learning for sequence labeling"

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)