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: "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.

Connected through hyperedge HE-1 (induced by source paper "using a kg copy network for non goal oriented dialogues"):
  with members:
    - "incorporating joint embeddings into goal oriented dialogues with multi task learning"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "a trustworthy responsible and interpretable system to handle chit chat in conversational bots"
    - "a knowledge grounded neural conversation model"
    - "chat detection in an intelligent assistant combining task oriented and non task oriented spoken dialogue systems"
    - "flexible end to end dialogue system for knowledge grounded conversation"

Connected through hyperedge HE-2 (induced by source paper "improving response selection in multi turn dialogue systems by incorporating domain knowledge"):
  with members:
    - "learning to compute word embeddings on the fly"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "topic augmented neural network for short text conversation"
    - "enriching word vectors with subword information"
    - "sentence pair scoring towards unified framework for text comprehension"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "learning end to end goal oriented dialog"

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)