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 multichannel convolutional neural network for cross language dialog state tracking"
  Abstract: The fifth Dialog State Tracking Challenge (DSTC5) introduces a new cross-language dialog state tracking scenario, where the participants are asked to build their trackers based on the English training corpus, while evaluating them with the unlabeled Chinese corpus. Although the computer-generated translations for both English and Chinese corpus are provided in the dataset, these translations contain errors and careless use of them can easily hurt the performance of the built trackers. To address this problem, we propose a multichannel Convolutional Neural Networks (CNN) architecture, in which we treat English and Chinese language as different input channels of one single CNN model. In the evaluation of DSTC5, we found that such multichannel architecture can effectively improve the robustness against translation errors. Additionally, our method for DSTC5 is purely machine learning based and requires no prior knowledge about the target language. We consider this a desirable property for building a tracker in the cross-language context, as not every developer will be familiar with both languages.

Connected through hyperedge HE-1 (induced by source paper "emotion detection on tv show transcripts with sequence based convolutional neural networks"):
  with members:
    - "wassa 2017 shared task on emotion intensity"
    - "deep heterogeneous feature fusion for template based face recognition"
    - "convolutional sequence to sequence learning"
    - "attentive pooling networks"
    - "recurrent neural network for text classification with multi task learning"
    - "show attend and tell neural image caption generation with visual attention"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "teaching machines to read and comprehend"

Connected through hyperedge HE-2 (induced by source paper "scalable multi domain dialogue state tracking"):
  with members:
    - "towards zero shot frame semantic parsing for domain scaling"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"
    - "a network based end to end trainable task oriented dialogue system"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "robust dialog state tracking for large ontologies"
    - "neural belief tracker data driven dialogue state tracking"
    - "multi domain dialog state tracking using recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "deep learning for extracting protein protein interactions from biomedical literature"):
  with members:
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)