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: "building sequential inference models for end to end response selection"
  Abstract: This paper presents an end-to-end response selection model for Track 1 of the 7th Dialogue System Technology Challenges (DSTC7). This task focuses on selecting the correct next utterance from a set of candidates given a partial conversation. We propose an end-to-end neural network based on enhanced sequential inference model (ESIM) for this task. Our proposed model differs from the original ESIM model in the following four aspects. First, a new word representation method which combines the general pre-trained word embeddings with those estimated on the task-specific training set is adopted in order to address the challenge of out-of-vocabulary (OOV) words. Second, an attentive hierarchical recurrent encoder (AHRE) is designed which is capable to encode sentences hierarchically and generate more descriptive representations by aggregation. Third, a new pooling method which combines multi-dimensional pooling and last-state pooling is used instead of the simple combination of max pooling and average pooling in the original ESIM. Last, a modification layer is added before the softmax layer to emphasize the importance of the last utterance in the context for response selection. In the released evaluation results of DSTC7, our proposed method ranked second on the Ubuntu dataset and third on the Advising dataset in subtask 1 of Track 1.

Connected through hyperedge HE-1 (induced by source paper "interactive matching network for multi turn response selection in retrieval based chatbots"):
  with members:
    - "deep contextualized word representations"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
    - "end to end neural coreference resolution"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "augmenting end to end dialog systems with commonsense knowledge"
    - "bilateral multi perspective matching for natural language sentences"
    - "learning to rank question answer pairs using hierarchical recurrent encoder with latent topic clustering"

Connected through hyperedge HE-2 (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"
    - "interactive matching network for multi turn response selection in retrieval based chatbots"
    - "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"

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)