human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "leveraging prior knowledge for protein protein interaction extraction with memory network"
  Abstract: Automatically extracting Protein-Protein Interactions (PPI) from biomedical literature provides additional support for precision medicine efforts. This paper proposes a novel memory network-based model (MNM) for PPI extraction, which leverages prior knowledge about protein-protein pairs with memory networks. The proposed MNM captures important context clues related to knowledge representations learned from knowledge bases. Both entity embeddings and relation embeddings of prior knowledge are effective in improving the PPI extraction model, leading to a new state-of-the-art performance on the BioCreative VI PPI dataset. The paper also shows that multiple computational layers over an external memory are superior to long short-term memory networks with the local memories.

Connected through hyperedge HE-1 (induced by source paper "improving neural protein protein interaction extraction with knowledge selection"):
  with members:
    - "chemical induced disease relation extraction with dependency information and prior knowledge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "end to end memory networks"
    - "deep residual learning for image recognition"
    - "neural responding machine for short text conversation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "knowledge aware attention network for protein protein interaction extraction"):
  with members:
    - "attention is all you need"
    - "a structured self attentive sentence embedding"
    - "a deep reinforced model for abstractive summarization"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "end to end memory networks"
    - "deep residual learning for image recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "graph space embedding"):
  with members:
    - "semi supervised classification with graph convolutional networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "adam a method for stochastic optimization"
    - "subgraph matching kernels for attributed graphs"

Question: Please tell me which class the center node 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)