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: "automatic question answering using a deep similarity neural network"
  Abstract: Automatic question-answering is a classical problem in natural language processing, which aims at designing systems that can automatically answer a question, in the same way as human does. In this work, we propose a deep learning based model for automatic question-answering. First the questions and answers are embedded using neural probabilistic modeling. Then a deep similarity neural network is trained to find the similarity score of a pair of answer and question. Then for each question, the best answer is found as the one with the highest similarity score. We first train this model on a large-scale public question-answering database, and then fine-tune it to transfer to the customer-care chat data. We have also tested our framework on a public question-answering database and achieved very good performance.

Connected through hyperedge HE-1 (induced by source paper "deep sentiment sentiment analysis using ensemble of cnn and bi lstm models"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "deep learning for hate speech detection in tweets"
    - "mask r cnn"
    - "deep reinforcement learning for dialogue generation"
    - "ssd single shot multibox detector"
    - "the darpa twitter bot challenge"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-2 (induced by source paper "deepiris iris recognition using a deep learning approach"):
  with members:
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "a deep unsupervised learning approach toward mtbi identification using diffusion mri"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "face recognition using scattering convolutional network"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "iris recognition using scattering transform and textural features"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "incremental improvement of a question answering system by re ranking answer candidates using machine learning"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "starspace embed all the things"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "ask the right questions active question reformulation with reinforcement learning"
    - "rasa open source language understanding and dialogue management"
    - "reading wikipedia to answer open domain questions"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-4 (induced by source paper "fingernet pushing the limits of fingerprint recognition using convolutional neural network"):
  with members:
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "a deep unsupervised learning approach toward mtbi identification using diffusion mri"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "face recognition using scattering convolutional network"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "iris recognition using scattering transform and textural features"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "ad net audio visual convolutional neural network for advertisement detection in videos"):
  with members:
    - "an experimental study of deep convolutional features for iris recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "show attend and tell neural image caption generation with visual attention"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)