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: "dynamic transfer learning for named entity recognition"
  Abstract: State-of-the-art named entity recognition (NER) systems have been improving continuously using neural architectures over the past several years. However, many tasks including NER require large sets of annotated data to achieve such performance. In particular, we focus on NER from clinical notes, which is one of the most fundamental and critical problems for medical text analysis. Our work centers on effectively adapting these neural architectures towards low-resource settings using parameter transfer methods. We complement a standard hierarchical NER model with a general transfer learning framework consisting of parameter sharing between the source and target tasks, and showcase scores significantly above the baseline architecture. These sharing schemes require an exponential search over tied parameter sets to generate an optimal configuration. To mitigate the problem of exhaustively searching for model optimization, we propose the Dynamic Transfer Networks (DTN), a gated architecture which learns the appropriate parameter sharing scheme between source and target datasets. DTN achieves the improvements of the optimized transfer learning framework with just a single training setting, effectively removing the need for exponential search.

Connected through hyperedge HE-1 (induced by source paper "assessment of amazon comprehend medical medication information extraction"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"

Connected through hyperedge HE-2 (induced by source paper "comprehend medical a named entity recognition and relationship extraction web service"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "a walk based model on entity graphs for relation extraction"
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "a hybrid deep learning approach for medical relation extraction"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "joint extraction of entities and relations based on a novel tagging scheme"

Connected through hyperedge HE-3 (induced by source paper "joint entity extraction and assertion detection for clinical text"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "comprehend medical a named entity recognition and relationship extraction web service"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "negbio a high performance tool for negation and uncertainty detection in radiology reports"
    - "morphological priors for probabilistic neural word embeddings"
    - "enriching word vectors with subword information"
    - "multi task cross lingual sequence tagging from scratch"
    - "multi task domain adaptation for sequence tagging"

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.LG(Machine Learning)