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 "a multi task learning approach for named entity recognition using local detection"):
  Abstract: Named entity recognition (NER) systems that perform well require task-related and manually annotated datasets. However, they are expensive to develop, and are thus limited in size. As there already exists a large number of NER datasets that share a certain degree of relationship but differ in content, it is important to explore the question of whether such datasets can be combined as a simple method for improving NER performance. To investigate this, we developed a novel locally detecting multitask model using FFNNs. The model relies on encoding variable-length sequences of words into theoretically lossless and unique fixed-size representations. We applied this method to several well-known NER tasks and compared the results of our model to baseline models as well as other published results. As a result, we observed competitive performance in nearly all of the tasks.

Includes members:
  Member "identifying beneficial task relations for multi task learning in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bam born again multi task networks for natural language understanding")
  Member "fast and accurate entity recognition with iterated dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "neural networks models for entity discovery and linking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a general fofe net framework for simple and effective question answering over knowledge bases")
  Member "when is multitask learning effective semantic sequence prediction under varying data conditions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parsing thai social data a new challenge for thai nlp")
  Member "multi task cross lingual sequence tagging from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi task learning for argumentation mining")
  Member "neural architectures for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graphs")
  Member "multilingual language processing from bytes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation")
  Member "a fixed size encoding method for variable length sequences with its application to neural network language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "effective context and fragment feature usage for named entity recognition")

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.CL(Computation and Language)