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 "the lifted matrix space model for semantic composition"):
  Abstract: Recent advances in tree structured sentence encoding models have shown that explicitly modeling syntax can help handle compositionality. More specifically, recent works by \citetext{Socher2012}, \citetext{Socher2013}, and \citetext{Chen2013} have shown that using more powerful composition functions with multiplicative interactions within tree-structured models can yield significant improvements in model performance. However, existing compositional approaches which make use of these multiplicative interactions usually have to learn task-specific matrix-shaped word embeddings or rely on third-order tensors, which can be very costly. This paper introduces the Lifted Matrix-Space model which improves on the predecessors on this aspect. The model learns a global transformation from pre-trained word embeddings into matrices, which can be composed via matrix multiplication. The upshot is that we can capture the multiplicative interaction without learning matrix-valued word representations from scratch. In addition, our composition function effectively transmits a larger number of activations across layers with comparably few model parameters. We evaluate our model on the Stanford NLI corpus and the Multi-Genre NLI corpus and find that the Lifted Matrix-Space model outperforms the tree-structured long short-term memory networks.

Includes members:
  Member "structural attention neural networks for improved sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a framework for end to end learning on semantic tree structured data")
  Member "natural language inference over interaction space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial nli a new benchmark for natural language understanding")
  Member "shortcut stacked sentence encoders for multi domain inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "a broad coverage challenge corpus for sentence understanding through inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "neural semantic encoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching")
  Member "aligning packed dependency trees a theory of composition for distributional semantics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "temporal and aspectual entailment")
  Member "long short term memory over tree structures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised recurrent neural network grammars")
  Member "compositional distributional semantics with long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "structure tree lstm structure aware attentional document encoders")

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)