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 "combining distant and direct supervision for neural relation extraction"):
  Abstract: In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model's ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method achieves a new state-of-the-art result on the widely used FB-NYT dataset.

Includes members:
  Member "allennlp a deep semantic natural language processing platform":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")
  Member "reside improving distantly supervised neural relation extraction using side information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural graph embedding methods for natural language processing")
  Member "zero shot sequence labeling transferring knowledge from sentences to tokens":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sequence labeling parsing by learning across representations")
  Member "neural relation extraction via inner sentence noise reduction and transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fine tuning pre trained transformer language models to distantly supervised relation extraction")
  Member "multi level structured self attentions for distantly supervised relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving distant supervised relation extraction by dynamic neural network")
  Member "deep residual learning for weakly supervised relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "long tail relation extraction via knowledge graph embeddings and graph convolution networks")
  Member "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dsreg using distant supervision as a regularizer")
  Member "neural discourse structure for text categorization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")

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)