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: "knowledge as a teacher knowledge guided structural attention networks"
  Abstract: Natural language understanding (NLU) is a core component of a spoken dialogue system. Recently recurrent neural networks (RNN) obtained strong results on NLU due to their superior ability of preserving sequential information over time. Traditionally, the NLU module tags semantic slots for utterances considering their flat structures, as the underlying RNN structure is a linear chain. However, natural language exhibits linguistic properties that provide rich, structured information for better understanding. This paper introduces a novel model, knowledge-guided structural attention networks (K-SAN), a generalization of RNN to additionally incorporate non-flat network topologies guided by prior knowledge. There are two characteristics: 1) important substructures can be captured from small training data, allowing the model to generalize to previously unseen test data; 2) the model automatically figures out the salient substructures that are essential to predict the semantic tags of the given sentences, so that the understanding performance can be improved. The experiments on the benchmark Air Travel Information System (ATIS) data show that the proposed K-SAN architecture can effectively extract salient knowledge from substructures with an attention mechanism, and outperform the performance of the state-of-the-art neural network based frameworks.

Connected through hyperedge HE-1 (induced by source paper "improving relation extraction with knowledge attention"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "position aware self attention with relative positional encodings for slot filling"
    - "on2vec embedding based relation prediction for ontology population"
    - "multi level structured self attentions for distantly supervised relation extraction"
    - "improved neural relation detection for knowledge base question answering"
    - "neural natural language inference models enhanced with external knowledge"

Connected through hyperedge HE-2 (induced by source paper "joint learning of word and label embeddings for sequence labelling in spoken language understanding"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "joint embedding of words and labels for text classification"
    - "improving slot filling performance with attentive neural networks on dependency structures"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "dynamically context sensitive time decay attention for dialogue modeling"):
  with members:
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "end to end task completion neural dialogue systems"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "dynamic memory networks for visual and textual question answering"
    - "learning end to end goal oriented dialog"

Connected through hyperedge HE-4 (induced by source paper "dynamic time aware attention to speaker roles and contexts for spoken language understanding"):
  with members:
    - "end to end task completion neural dialogue systems"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "dynamic memory networks for visual and textual question answering"
    - "end to end joint learning of natural language understanding and dialogue manager"
    - "learning end to end goal oriented dialog"
    - "end to end memory networks"

Connected through hyperedge HE-5 (induced by source paper "speaker role contextual modeling for language understanding and dialogue policy learning"):
  with members:
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "end to end joint learning of natural language understanding and dialogue manager"
    - "learning end to end goal oriented dialog"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "real time on demand crowd powered entity extraction"):
  with members:
    - "leveraging semantic web search and browse sessions for multi turn spoken dialog systems"

Connected through hyperedge HE-7 (induced by source paper "end to end joint learning of natural language understanding and dialogue manager"):
  with members:
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "adam a method for stochastic optimization"

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.AI(Artificial Intelligence)