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 "relation adversarial network for low resource knowledgegraph completion"):
  Abstract: Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficulties for KGC is the low resource problem. Previous approaches assume sufficient training triples to learn versatile vectors for entities and relations, or a satisfactory number of labeled sentences to train a competent relation extraction model. However, low resource relations are very common in KGs, and those newly added relations often do not have many known samples for training. In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available. We propose a general framework called Weighted Relation Adversarial Network, which utilizes an adversarial procedure to help adapt knowledge/features learned from high resource relations to different but related low resource relations. Specifically, the framework takes advantage of a relation discriminator to distinguish between samples from different relations, and help learn relation-invariant features more transferable from source relations to target relations. Experimental results show that the proposed approach outperforms previous methods regarding low resource settings for both link prediction and relation extraction.

Includes members:
  Member "long tail relation extraction via knowledge graph embeddings and graph convolution networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "open knowledge enrichment for long tail entities")
  Member "multi task feature learning for knowledge graph enhanced recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "entity context and relational paths for knowledge graph completion")
  Member "rotate knowledge graph embedding by relational rotation in complex space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "kg bert bert for knowledge graph completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")
  Member "ernie enhanced language representation with informative entities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "iteratively learning embeddings and rules for knowledge graph reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "entity context and relational paths for knowledge graph completion")
  Member "transfer learning for relation extraction via relation gated adversarial learning":
    also connected through other hyperedges:
      (none)
  Member "meta relational learning for few shot link prediction in knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")

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)