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 "improving retrieval modeling using cross convolution networks and multi frequency word embedding"):
  Abstract: To build a satisfying chatbot that has the ability of managing a goal-oriented multi-turn dialogue, accurate modeling of human conversation is crucial. In this paper we concentrate on the task of response selection for multi-turn human-computer conversation with a given context. Previous approaches show weakness in capturing information of rare keywords that appear in either or both context and correct response, and struggle with long input sequences. We propose Cross Convolution Network (CCN) and Multi Frequency word embedding to address both problems. We train several models using the Ubuntu Dialogue dataset which is the largest freely available multi-turn based dialogue corpus. We further build an ensemble model by averaging predictions of multiple models. We achieve a new state-of-the-art on this dataset with considerable improvements compared to previous best results.

Includes members:
  Member "self guiding multimodal lstm when we do not have a perfect training dataset for image captioning":
    also connected through other hyperedges:
      (none)
  Member "on the evaluation of dialogue systems with next utterance classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pretraining methods for dialog context representation learning")
  Member "sentence pair scoring towards unified framework for text comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "training neural response selection for task oriented dialogue systems")
  Member "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "incorporating loose structured knowledge into conversation modeling via recall gate lstm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "conversation generation with concept flow")
  Member "syntax based deep matching of short texts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised context rewriting for open domain conversation")
  Member "improved deep learning baselines for ubuntu corpus dialogs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sequential neural networks for noetic end to end response selection")
  Member "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")

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)