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: "syntax based deep matching of short texts"
  Abstract: Many tasks in natural language processing, ranging from machine translation to question answering, can be reduced to the problem of matching two sentences or more generally two short texts. We propose a new approach to the problem, called Deep Match Tree (DeepMatch$_{tree}$), under a general setting. The approach consists of two components, 1) a mining algorithm to discover patterns for matching two short-texts, defined in the product space of dependency trees, and 2) a deep neural network for matching short texts using the mined patterns, as well as a learning algorithm to build the network having a sparse structure. We test our algorithm on the problem of matching a tweet and a response in social media, a hard matching problem proposed in [Wang et al., 2013], and show that DeepMatch$_{tree}$ can outperform a number of competitor models including one without using dependency trees and one based on word-embedding, all with large margins

Connected through hyperedge HE-1 (induced by source paper "unsupervised context rewriting for open domain conversation"):
  with members:
    - "response generation by context aware prototype editing"
    - "a study of reinforcement learning for neural machine translation"
    - "hierarchical recurrent attention network for response generation"
    - "a persona based neural conversation model"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "topic aware neural response generation"

Connected through hyperedge HE-2 (induced by source paper "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"):
  with members:
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "co sampling training robust networks for extremely noisy supervision"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "a sequential matching framework for multi turn response selection in retrieval based chatbots"
    - "fidelity weighted learning"

Connected through hyperedge HE-3 (induced by source paper "triplenet triple attention network for multi turn response selection in retrieval based chatbots"):
  with members:
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "deep contextualized word representations"
    - "a structured self attentive sentence embedding"
    - "bidirectional attention flow for machine comprehension"
    - "on the evaluation of dialogue systems with next utterance classification"
    - "sentence pair scoring towards unified framework for text comprehension"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "match srnn modeling the recursive matching structure with spatial rnn"

Connected through hyperedge HE-4 (induced by source paper "improving matching models with hierarchical contextualized representations for multi turn response selection"):
  with members:
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "deep contextualized word representations"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "hierarchical recurrent attention network for response generation"
    - "semi supervised sequence tagging with bidirectional language models"

Connected through hyperedge HE-5 (induced by source paper "improving retrieval modeling using cross convolution networks and multi frequency word embedding"):
  with members:
    - "self guiding multimodal lstm when we do not have a perfect training dataset for image captioning"
    - "on the evaluation of dialogue systems with next utterance classification"
    - "sentence pair scoring towards unified framework for text comprehension"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "incorporating loose structured knowledge into conversation modeling via recall gate lstm"
    - "improved deep learning baselines for ubuntu corpus dialogs"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "a neural network approach to context sensitive generation of conversational responses"

Connected through hyperedge HE-6 (induced by source paper "learning matching models with weak supervision for response selection in retrieval based chatbots"):
  with members:
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "hierarchical recurrent attention network for response generation"
    - "avoiding your teacher s mistakes training neural networks with controlled weak supervision"
    - "fidelity weighted learning"
    - "topic aware neural response generation"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-7 (induced by source paper "addressee and response selection for multilingual conversation"):
  with members:
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "a persona based neural conversation model"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "overcoming catastrophic forgetting in neural networks"
    - "massively multilingual word embeddings"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "neural responding machine for short text conversation"

Connected through hyperedge HE-8 (induced by source paper "a sequential matching framework for multi turn response selection in retrieval based chatbots"):
  with members:
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "adversarial learning for neural dialogue generation"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "a decomposable attention model for natural language inference"
    - "multiresolution recurrent neural networks an application to dialogue response generation"

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)