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 "attention based capsule networks with dynamic routing for relation extraction"):
  Abstract: A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.

Includes members:
  Member "investigating capsule networks with dynamic routing for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gret global representation enhanced transformer")
  Member "ensemble neural relation extraction with adaptive boosting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi labeled relation extraction with attentive capsule network")
  Member "improving distantly supervised relation extraction using word and entity based attention":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural graph embedding methods for natural language processing")
  Member "information aggregation via dynamic routing for sequence encoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "detecting fake news with capsule neural networks")
  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 "cross sentence n ary relation extraction with graph lstms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive fractional order graph neural network")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "a structured self attentive sentence embedding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "high order paired aspp networks for semantic segmenation")

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.IR(Information Retrieval)