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 "distantly supervised named entity recognition using positive unlabeled learning"):
  Abstract: In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive-unlabeled (PU) learning problem and accordingly propose a novel PU learning algorithm to perform the task. We prove that the proposed algorithm can unbiasedly and consistently estimate the task loss as if there is fully labeled data. A key feature of the proposed method is that it does not require the dictionaries to label every entity within a sentence, and it even does not require the dictionaries to label all of the words constituting an entity. This greatly reduces the requirement on the quality of the dictionaries and makes our method generalize well with quite simple dictionaries. Empirical studies on four public NER datasets demonstrate the effectiveness of our proposed method. We have published the source code at \url{this https URL}.

Includes members:
  Member "chinese ner using lattice lstm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self attention based bigru and capsule network for named entity recognition")
  Member "positive unlabeled learning with non negative risk estimator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection as a positive unlabeled problem")
  Member "empower sequence labeling with task aware neural language model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "keeping it simple implementation and performance of the proto principle of adaptation and learning in the language sciences")
  Member "neural architectures for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "knowledge graphs")
  Member "bidirectional lstm crf models for sequence tagging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "named entity recognition with bidirectional lstm cnns":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "large scale simple question answering with memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "pu learning for matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "advances in collaborative filtering and ranking")

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)