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 "deep ranking based cost sensitive multi label learning for distant supervision relation extraction"):
  Abstract: Knowledge base provides a potential way to improve the intelligence of information retrieval (IR) systems, for that knowledge base has numerous relations between entities which can help the IR systems to conduct inference from one entity to another entity. Relation extraction is one of the fundamental techniques to construct a knowledge base. Distant supervision is a semi-supervised learning method for relation extraction which learns with labeled and unlabeled data. However, this approach suffers the problem of relation overlapping in which one entity tuple may have multiple relation facts. We believe that relation types can have latent connections, which we call class ties, and can be exploited to enhance relation extraction. However, this property between relation classes has not been fully explored before. In this paper, to exploit class ties between relations to improve relation extraction, we propose a general ranking based multi-label learning framework combined with convolutional neural networks, in which ranking based loss functions with regularization technique are introduced to learn the latent connections between relations. Furthermore, to deal with the problem of class imbalance in distant supervision relation extraction, we further adopt cost-sensitive learning to rescale the costs from the positive and negative labels. Extensive experiments on a widely used dataset show the effectiveness of our model to exploit class ties and to relieve class imbalance problem.

Includes members:
  Member "jointly learning semantic parser and natural language generator via dual information maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging code generation to improve code retrieval and summarization via dual learning")
  Member "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "jec qa a legal domain question answering dataset")
  Member "dsgan generative adversarial training for distant supervision relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep self supervised representation learning for free hand sketch")
  Member "robust distant supervision relation extraction via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep self supervised representation learning for free hand sketch")
  Member "semi supervised learning for neural keyphrase generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "encoding implicit relation requirements for relation extraction a joint inference approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "integrating relation constraints with neural relation extractors")
  Member "reinforcement learning for relation classification from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dialog state tracking with reinforced data augmentation")
  Member "denoising distant supervision for relation extraction via instance level adversarial training":
    also connected through other hyperedges:
      (none)

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)