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: "sliced recurrent neural networks"
  Abstract: Recurrent neural networks have achieved great success in many NLP tasks. However, they have difficulty in parallelization because of the recurrent structure, so it takes much time to train RNNs. In this paper, we introduce sliced recurrent neural networks (SRNNs), which could be parallelized by slicing the sequences into many subsequences. SRNNs have the ability to obtain high-level information through multiple layers with few extra parameters. We prove that the standard RNN is a special case of the SRNN when we use linear activation functions. Without changing the recurrent units, SRNNs are 136 times as fast as standard RNNs and could be even faster when we train longer sequences. Experiments on six largescale sentiment analysis datasets show that SRNNs achieve better performance than standard RNNs.

Connected through hyperedge HE-1 (induced by source paper "large scale answerer in questioner s mind for visual dialog question generation"):
  with members:
    - "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "mine mutual information neural estimation"
    - "nsml meet the mlaas platform with a real world case study"
    - "two can play this game visual dialog with discriminative question generation and answering"

Connected through hyperedge HE-2 (induced by source paper "incremental reading for question answering"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "reasonet learning to stop reading in machine comprehension"
    - "learning to skim text"
    - "simple and effective multi paragraph reading comprehension"
    - "learning when to skim and when to read"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-3 (induced by source paper "deep landscape forecasting for real time bidding advertising"):
  with members:
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "deep learning for patient specific kidney graft survival analysis"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "product based neural networks for user response prediction"
    - "architectural complexity measures of recurrent neural networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "real time bidding benchmarking with ipinyou dataset"
    - "real time bidding for online advertising measurement and analysis"

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)