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 "diag nre a neural pattern diagnosis framework for distantly supervised neural relation extraction"):
  Abstract: Pattern-based labeling methods have achieved promising results in alleviating the inevitable labeling noises of distantly supervised neural relation extraction. However, these methods require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize this http URL ease the labor-intensive workload of pattern writing and enable the quick generalization to new relation types, we propose a neural pattern diagnosis framework, DIAG-NRE, that can automatically summarize and refine high-quality relational patterns from noise data with human experts in the loop. To demonstrate the effectiveness of DIAG-NRE, we apply it to two real-world datasets and present both significant and interpretable improvements over state-of-the-art methods.

Includes members:
  Member "robust distant supervision relation extraction via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "dialog state tracking with reinforced data augmentation")
  Member "neural open information extraction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hybrid neural tagging model for open relation extraction")
  Member "metapad meta pattern discovery from massive text corpora":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "taxoexpan self supervised taxonomy expansion with position enhanced graph neural network")
  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 "snorkel rapid training data creation with weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records")
  Member "heterogeneous supervision for relation extraction a representation learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")
  Member "learning the structure of generative models without labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors")

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)