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 "aspect based sentiment classification with aspect specific graph convolutional networks"):
  Abstract: Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification. However, these models lack a mechanism to account for relevant syntactical constraints and long-range word dependencies, and hence may mistakenly recognize syntactically irrelevant contextual words as clues for judging aspect sentiment. To tackle this problem, we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspect-specific sentiment classification framework is raised. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness to a range of state-of-the-art models, and further demonstrate that both syntactical information and long-range word dependencies are properly captured by the graph convolution structure.

Includes members:
  Member "dating documents using graph convolution networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asap adaptive structure aware pooling for learning hierarchical graph representations")
  Member "graph convolution over pruned dependency trees improves relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters")
  Member "transformation networks for target oriented sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network")
  Member "graph convolutional networks for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta embeddings based on self attention")
  Member "aspect level sentiment classification with attention over attention neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network")
  Member "aspect based sentiment analysis with gated convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "component analysis for visual question answering architectures")
  Member "encoding sentences with graph convolutional networks for semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "benchmarking graph neural networks")
  Member "interactive attention networks for aspect level sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network")

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)