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: "a deep architecture for semantic parsing"
  Abstract: Many successful approaches to semantic parsing build on top of the syntactic analysis of text, and make use of distributional representations or statistical models to match parses to ontology-specific queries. This paper presents a novel deep learning architecture which provides a semantic parsing system through the union of two neural models of language semantics. It allows for the generation of ontology-specific queries from natural language statements and questions without the need for parsing, which makes it especially suitable to grammatically malformed or syntactically atypical text, such as tweets, as well as permitting the development of semantic parsers for resource-poor languages.

Connected through hyperedge HE-1 (induced by source paper "rehar robust and efficient human activity recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "convolutional two stream network fusion for video action recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-2 (induced by source paper "language to logical form with neural attention"):
  with members:
    - "modeling coverage for neural machine translation"
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "a neural attention model for abstractive sentence summarization"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "visualizing and understanding recurrent networks"
    - "teaching machines to read and comprehend"

Connected through hyperedge HE-3 (induced by source paper "data recombination for neural semantic parsing"):
  with members:
    - "pointing the unknown words"
    - "language to logical form with neural attention"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"
    - "effective approaches to attention based neural machine translation"
    - "transition based dependency parsing with stack long short term memory"
    - "traversing knowledge graphs in vector space"
    - "type driven incremental semantic parsing with polymorphism"

Connected through hyperedge HE-4 (induced by source paper "joint learning of ontology and semantic parser from text"):
  with members:
    - "interactive knowledge base population"
    - "learning dependency based compositional semantics"

Connected through hyperedge HE-5 (induced by source paper "intelligent management and efficient operation of big data"):
  with members:
    - "software defined networking a comprehensive survey"
    - "undefined by data a survey of big data definitions"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-6 (induced by source paper "unifying visual semantic embeddings with multimodal neural language models"):
  with members:
    - "a multiplicative model for learning distributed text based attribute representations"
    - "very deep convolutional networks for large scale image recognition"
    - "multilingual models for compositional distributed semantics"
    - "explain images with multimodal recurrent neural networks"
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "neural machine translation by jointly learning to align and translate"
    - "recurrent neural network regularization"
    - "microsoft coco common objects in context"

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)