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: "label aware double transfer learning for cross specialty medical named entity recognition"
  Abstract: We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and the cost of human annotation makes it particularly difficult to train a universal medical NER system. In this paper, we propose a label-aware double transfer learning framework (La-DTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts. The transferability is guaranteed by two components: (i) we propose label-aware MMD for feature representation transfer, and (ii) we perform parameter transfer with a theoretical upper bound which is also label aware. We conduct extensive experiments on 12 cross-specialty NER tasks. The experimental results demonstrate that La-DTL provides consistent accuracy improvement over strong baselines. Besides, the promising experimental results on non-medical NER scenarios indicate that La-DTL is potential to be seamlessly adapted to a wide range of NER 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"
    - "neural adaptation layers for cross domain named entity recognition"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-2 (induced by source paper "few shot classification in named entity recognition task"):
  with members:
    - "sequence labeling a practical approach"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "learning how to self learn enhancing self training using neural reinforcement learning"
    - "neural cross lingual named entity recognition with minimal resources"
    - "deep active learning for named entity recognition"
    - "prototypical networks for few shot learning"
    - "improving one shot learning through fusing side information"

Connected through hyperedge HE-3 (induced by source paper "dynamic transfer learning for named entity recognition"):
  with members:
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "the natural language decathlon multitask learning as question answering"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "dynamic multi level multi task learning for sentence simplification"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"

Connected through hyperedge HE-4 (induced by source paper "neural adaptation layers for cross domain named entity recognition"):
  with members:
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised sequence tagging with bidirectional language models"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "transfer learning for named entity recognition with neural networks"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "how transferable are neural networks in nlp applications"

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)