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 "uncover the ground truth relations in distant supervision a neural expectation maximization framework"):
  Abstract: Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts. Nevertheless, most of the prior-art models for such tasks assume that the given text can be noisy, but their corresponding labels are clean. Such unrealistic assumption is contradictory with the fact that the given labels are often noisy as well, thus leading to significant performance degradation of those models on real-world data. To cope with this challenge, we propose a novel label-denoising framework that combines neural network with probabilistic modelling, which naturally takes into account the noisy labels during learning. We empirically demonstrate that our approach significantly improves the current art in uncovering the ground-truth relation labels.

Includes members:
  Member "dsgan generative adversarial training for distant supervision relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "deep self supervised representation learning for free hand sketch")
  Member "reinforcement learning for relation classification from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dialog state tracking with reinforced data augmentation")
  Member "weakly supervised relation extraction by pattern enhanced embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic logic neural networks for reasoning")
  Member "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dsreg using distant supervision as a regularizer")
  Member "neural expectation maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "space unsupervised object oriented scene representation via spatial attention and decomposition")
  Member "jointly extracting relations with class ties via effective deep ranking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relation adversarial network for low resource knowledgegraph completion")
  Member "end to end relation extraction using lstms on sequences and tree structures":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deeper task specificity improves joint entity and relation extraction")

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)