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: "topic augmented neural network for short text conversation"
  Abstract: Message response matching is an important task within retrieval-based chatbots. We present a topic augmented neural network(TANN), comprising a sentence embedding layer, a topic embedding layer, and a matching layer, to match messages and response candidates. TANN inherits the benefits of neutral networks on matching sentence pairs, and leverages extra topic information and their corresponding weights as prior knowledge into a matching process. In TANN, the sentence embedding layer embeds an input message and a response into a vector space, while the topic embedding layer forms a topic vector by a linear combination of the embedding of topic words whose weights are determined by both themselves and the message vector. The message vector, the response vector, and the topic vector are then fed to the matching layer to calculate a matching score. The extensive evaluation of TANN, using large human annotated data sets, shows that TANN outperforms simple neutral network methods, while beating other typical matching models with a large margin.

Connected through hyperedge HE-1 (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"
    - "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"
    - "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-2 (induced by source paper "bidirectional attention for sql generation"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "an encoder decoder framework translating natural language to database queries"
    - "latent attention for if then program synthesis"
    - "language to logical form with neural attention"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"):
  with members:
    - "a persona based neural conversation model"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "topic augmented neural response generation with a joint attention mechanism"
    - "incorporating loose structured knowledge into conversation modeling via recall gate lstm"
    - "theano a python framework for fast computation of mathematical expressions"
    - "match srnn modeling the recursive matching structure with spatial rnn"
    - "syntax based deep matching of short texts"
    - "improved deep learning baselines for ubuntu corpus dialogs"

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)