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 "selective attention based graph convolutional networks for aspect level sentiment classification"):
  Abstract: Aspect-level sentiment classification aims to identify the sentiment polarity towards a specific aspect term in a sentence. Most current approaches mainly consider the semantic information by utilizing attention mechanisms to capture the interactions between the context and the aspect term. In this paper, we propose to employ graph convolutional networks (GCNs) on the dependency tree to learn syntax-aware representations of aspect terms. GCNs often show the best performance with two layers, and deeper GCNs do not bring additional gain due to over-smoothing problem. However, in some cases, important context words cannot be reached within two hops on the dependency tree. Therefore we design a selective attention based GCN block (SA-GCN) to find the most important context words, and directly aggregate these information into the aspect-term representation. We conduct experiments on the SemEval 2014 Task 4 datasets. Our experimental results show that our model outperforms the current state-of-the-art.

Includes members:
  Member "coarse grain fine grain coattention network for multi evidence question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "answering while summarizing multi task learning for multi hop qa with evidence extraction")
  Member "targeted sentiment classification with attentional encoder network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial training for aspect based sentiment analysis with bert")
  Member "densely connected graph convolutional networks for graph to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "depth adaptive graph recurrent network for text classification")
  Member "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tensor graph convolutional networks for text classification")
  Member "bert post training for review reading comprehension and aspect based sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "attention guided graph convolutional networks for relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "event detection with relation aware graph convolutional neural networks")
  Member "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial training for aspect based sentiment analysis with bert")
  Member "exploiting coarse to fine task transfer for aspect level sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bert post training for review reading comprehension and aspect based sentiment analysis")

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)