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: "interactive matching network for multi turn response selection in retrieval based chatbots"
  Abstract: In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task. First, IMN constructs word representations from three aspects to address the challenge of out-of-vocabulary (OOV) words. Second, an attentive hierarchical recurrent encoder (AHRE), which is capable of encoding sentences hierarchically and generating more descriptive representations by aggregating with an attention mechanism, is designed. Finally, the bidirectional interactions between whole multi-turn contexts and response candidates are calculated to derive the matching information between them. Experiments on four public datasets show that IMN outperforms the baseline models on all metrics, achieving a new state-of-the-art performance and demonstrating compatibility across domains for multi-turn response selection.

Connected through hyperedge HE-1 (induced by source paper "utterance to utterance interactive matching network for multi turn response selection in retrieval based chatbots"):
  with members:
    - "exploiting persona information for diverse generation of conversational responses"
    - "dually interactive matching network for personalized response selection in retrieval based chatbots"
    - "building sequential inference models for end to end response selection"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "end to end neural coreference resolution"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-2 (induced by source paper "domain adaptive training bert for response selection"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
    - "attention is all you need"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-3 (induced by source paper "dually interactive matching network for personalized response selection in retrieval based chatbots"):
  with members:
    - "training millions of personalized dialogue agents"
    - "a persona based neural conversation model"
    - "key value memory networks for directly reading documents"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "tensorflow a system for large scale machine learning"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

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)