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: "dependent gated reading for cloze style question answering"
  Abstract: We present a novel deep learning architecture to address the cloze-style question answering task. Existing approaches employ reading mechanisms that do not fully exploit the interdependency between the document and the query. In this paper, we propose a novel \emph{dependent gated reading} bidirectional GRU network (DGR) to efficiently model the relationship between the document and the query during encoding and decision making. Our evaluation shows that DGR obtains highly competitive performance on well-known machine comprehension benchmarks such as the Children's Book Test (CBT-NE and CBT-CN) and Who DiD What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate our model by ablation and attention studies.

Connected through hyperedge HE-1 (induced by source paper "saliency learning teaching the model where to pay attention"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "event detection with neural networks a rigorous empirical evaluation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "event nugget detection with forward backward recurrent neural networks"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "analyzing the behavior of visual question answering models"
    - "text understanding with the attention sum reader network"
    - "rationalizing neural predictions"

Connected through hyperedge HE-2 (induced by source paper "interpreting recurrent and attention based neural models a case study on natural language inference"):
  with members:
    - "deep contextualized word representations"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "natural language inference over interaction space"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "a compare propagate architecture with alignment factorization for natural language inference"
    - "learned in translation contextualized word vectors"
    - "bilateral multi perspective matching for natural language sentences"

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)