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 "explicit interaction model towards text classification"):
  Abstract: Text classification is one of the fundamental tasks in natural language processing. Recently, deep neural networks have achieved promising performance in the text classification task compared to shallow models. Despite of the significance of deep models, they ignore the fine-grained (matching signals between words and classes) classification clues since their classifications mainly rely on the text-level representations. To address this problem, we introduce the interaction mechanism to incorporate word-level matching signals into the text classification task. In particular, we design a novel framework, EXplicit interAction Model (dubbed as EXAM), equipped with the interaction mechanism. We justified the proposed approach on several benchmark datasets including both multi-label and multi-class text classification tasks. Extensive experimental results demonstrate the superiority of the proposed method. As a byproduct, we have released the codes and parameter settings to facilitate other researches.

Includes members:
  Member "natural language inference over interaction space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial nli a new benchmark for natural language understanding")
  Member "bilateral multi perspective matching for natural language sentences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")
  Member "a decomposable attention model for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "using the output embedding to improve language models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta embeddings based on self attention")
  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 "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "neural tree indexers for text understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ordered memory")
  Member "a compare aggregate model for matching text sequences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to respond with stickers a framework of unifying multi modality in multi turn dialog")

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)