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: "empirical study on deep learning models for question answering"
  Abstract: In this paper we explore deep learning models with memory component or attention mechanism for question answering task. We combine and compare three models, Neural Machine Translation, Neural Turing Machine, and Memory Networks for a simulated QA data set. This paper is the first one that uses Neural Machine Translation and Neural Turing Machines for solving QA tasks. Our results suggest that the combination of attention and memory have potential to solve certain QA problem.

Connected through hyperedge HE-1 (induced by source paper "end to end answer chunk extraction and ranking for reading comprehension"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "attentive pooling networks"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "a parallel hierarchical model for machine comprehension on sparse data"
    - "attention based convolutional neural network for machine comprehension"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"
    - "iterative alternating neural attention for machine reading"

Connected through hyperedge HE-2 (induced by source paper "deep recurrent models with fast forward connections for neural machine translation"):
  with members:
    - "deep residual learning for image recognition"
    - "grid long short term memory"
    - "teaching machines to read and comprehend"
    - "a neural conversational model"
    - "depth gated lstm"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "deep captioning with multimodal recurrent neural networks m rnn"

Connected through hyperedge HE-3 (induced by source paper "hierarchical memory networks for answer selection on unknown words"):
  with members:
    - "pointing the unknown words"
    - "dynamic memory networks for visual and textual question answering"
    - "end to end memory networks"
    - "applying deep learning to answer selection a study and an open task"
    - "neural responding machine for short text conversation"
    - "teaching machines to read and comprehend"
    - "large scale simple question answering with memory networks"
    - "neural turing machines"

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)