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: "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
  Abstract: Ubuntu dialogue corpus is the largest public available dialogue corpus to make it feasible to build end-to-end deep neural network models directly from the conversation data. One challenge of Ubuntu dialogue corpus is the large number of out-of-vocabulary words. In this paper we proposed an algorithm which combines the general pre-trained word embedding vectors with those generated on the task-specific training set to address this issue. We integrated character embedding into Chen et al's Enhanced LSTM method (ESIM) and used it to evaluate the effectiveness of our proposed method. For the task of next utterance selection, the proposed method has demonstrated a significant performance improvement against original ESIM and the new model has achieved state-of-the-art results on both Ubuntu dialogue corpus and Douban conversation corpus. In addition, we investigated the performance impact of end-of-utterance and end-of-turn token tags.

Connected through hyperedge HE-1 (induced by source paper "interactive matching network for multi turn response selection in retrieval based chatbots"):
  with members:
    - "building sequential inference models for end to end response selection"
    - "deep contextualized word representations"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "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 "knowledge incorporating esim models for response selection in retrieval based dialog systems"):
  with members:
    - "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
    - "key value retrieval networks for task oriented dialogue"
    - "a retrieval based dialogue system utilizing utterance and context embeddings"
    - "a knowledge grounded neural conversation model"
    - "augmenting end to end dialog systems with commonsense knowledge"
    - "enhanced lstm for natural language inference"
    - "lstm based mixture of experts for knowledge aware dialogues"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"

Connected through hyperedge HE-3 (induced by source paper "enriching conversation context in retrieval based chatbots"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "asking clarifying questions in open domain information seeking conversations"
    - "multi task deep neural networks for natural language understanding"
    - "knowledge incorporating esim models for response selection in retrieval based dialog systems"
    - "training millions of personalized dialogue agents"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "neural approaches to conversational ai"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"

Connected through hyperedge HE-4 (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"
    - "interactive matching network for multi turn response selection in retrieval based chatbots"
    - "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"
    - "attention is all you need"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-5 (induced by source paper "improving response selection in multi turn dialogue systems by incorporating domain knowledge"):
  with members:
    - "improving retrieval modeling using cross convolution networks and multi frequency word embedding"
    - "learning to compute word embeddings on the fly"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "building sequential inference models for end to end response selection"):
  with members:
    - "deep contextualized word representations"
    - "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
    - "bilateral multi perspective matching for natural language sentences"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "enhanced lstm for natural language inference"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "neural responding machine for short text conversation"

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)