human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "joint entity extraction and assertion detection for clinical text":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extracting umls concepts from medical text using general and domain specific deep learning models")
  Member "simultaneously self attending to all mentions for full abstract biological relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "leveraging dependency forest for neural medical relation extraction")
  Member "the natural language decathlon multitask learning as question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "soft layer specific multi task summarization with entailment and question generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to multi task learn for better neural machine translation")
  Member "label aware double transfer learning for cross specialty medical named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "zero resource cross domain named entity recognition")
  Member "improving hospital mortality prediction with medical named entities and multimodal learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "assessment of amazon comprehend medical medication information extraction")
  Member "multi task learning of pairwise sequence classification tasks over disparate label spaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beyond without forgetting multi task learning for classification with disjoint datasets")
  Member "dynamic multi level multi task learning for sentence simplification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "automatic compilation of resources for academic writing and evaluating with informal word identification and paraphrasing system")

Question: Please tell me which class the source paper 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)