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 "multi turn inference matching network for natural language inference"):
  Abstract: Natural Language Inference (NLI) is a fundamental and challenging task in Natural Language Processing (NLP). Most existing methods only apply one-pass inference process on a mixed matching feature, which is a concatenation of different matching features between a premise and a hypothesis. In this paper, we propose a new model called Multi-turn Inference Matching Network (MIMN) to perform multi-turn inference on different matching features. In each turn, the model focuses on one particular matching feature instead of the mixed matching feature. To enhance the interaction between different matching features, a memory component is employed to store the history inference information. The inference of each turn is performed on the current matching feature and the memory. We conduct experiments on three different NLI datasets. The experimental results show that our model outperforms or achieves the state-of-the-art performance on all the three datasets.

Includes members:
  Member "dr bilstm dependent reading bidirectional lstm for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "kernel graph attention network for fact verification")
  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 "natural language inference from multiple premises":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "don t take the premise for granted mitigating artifacts in natural language inference")
  Member "a compare propagate architecture with alignment factorization for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discourse marker augmented network with reinforcement learning for natural language inference")
  Member "learned in translation contextualized word vectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "shortcut stacked sentence encoders for multi domain inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching")
  Member "bilateral multi perspective matching for natural language sentences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")

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)