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 "integrating relation constraints with neural relation extractors"):
  Abstract: Recent years have seen rapid progress in identifying predefined relationship between entity pairs using neural networks NNs. However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints. These constraints are often defined over combinations of entity-relation-entity triples, since there often lack of explicitly well-defined type and cardinality requirements for the relations. In this paper, we propose a unified framework to integrate relation constraints with NNs by introducing a new loss term, ConstraintLoss. Particularly, we develop two efficient methods to capture how well the local predictions from multiple instance pairs satisfy the relation constraints. Experiments on both English and Chinese datasets show that our approach can help NNs learn from discrete relation constraints to reduce inconsistency among local predictions, and outperform popular neural relation extraction NRE models even enhanced with extra post-processing. Our source code and datasets will be released at this https URL.

Includes members:
  Member "lattice cnns for matching based chinese question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using local knowledge graph construction to scale seq2seq models to multi document inputs")
  Member "marrying up regular expressions with neural networks a case study for spoken language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalize symbolic knowledge with neural rule engine")
  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 "reside improving distantly supervised neural relation extraction using side information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural graph embedding methods for natural language processing")
  Member "see syntax aware entity embedding for neural relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relation adversarial network for low resource knowledgegraph completion")
  Member "encoding implicit relation requirements for relation extraction a joint inference approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep ranking based cost sensitive multi label learning for distant supervision relation extraction")
  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 "improved neural relation detection for knowledge base question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural relation prediction for simple question answering over knowledge graph")

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)