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 "improving distant supervised relation extraction by dynamic neural network"):
  Abstract: Distant Supervised Relation Extraction (DSRE) is usually formulated as a problem of classifying a bag of sentences that contain two query entities, into the predefined relation classes. Most existing methods consider those relation classes as distinct semantic categories while ignoring their potential connection to query entities. In this paper, we propose to leverage this connection to improve the relation extraction accuracy. Our key ideas are twofold: (1) For sentences belonging to the same relation class, the expression style, i.e. words choice, can vary according to the query entities. To account for this style shift, the model should adjust its parameters in accordance with entity types. (2) Some relation classes are semantically similar, and the entity types appear in one relation may also appear in others. Therefore, it can be trained cross different relation classes and further enhance those classes with few samples, i.e., long-tail classes. To unify these two arguments, we developed a novel Dynamic Neural Network for Relation Extraction (DNNRE). The network adopts a novel dynamic parameter generator that dynamically generates the network parameters according to the query entity types and relation classes. By using this mechanism, the network can simultaneously handle the style shift problem and enhance the prediction accuracy for long-tail classes. Through our experimental study, we demonstrate the effectiveness of the proposed method and show that it can achieve superior performance over the state-of-the-art methods.

Includes members:
  Member "global textual relation embedding for relational understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "few shot nlg with pre trained language model")
  Member "fine tuning pre trained transformer language models to distantly supervised relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "long tail relation extraction via knowledge graph embeddings and graph convolution networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "open knowledge enrichment for long tail entities")
  Member "cross relation cross bag attention for distantly supervised relation extraction":
    also connected through other hyperedges:
      (none)
  Member "distant supervision for relation extraction with linear attenuation simulation and non iid relevance embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "are noisy sentences useless for distant supervised relation extraction")
  Member "multi labeled relation extraction with attentive capsule network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network")
  Member "combining distant and direct supervision for neural relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structured minimally supervised learning for neural relation extraction")
  Member "reside improving distantly supervised neural relation extraction using side information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural graph embedding methods for natural language processing")

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)