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: "byte based language identification with deep convolutional networks"
  Abstract: We report on our system for the shared task on discriminating between similar languages (DSL 2016). The system uses only byte representations in a deep residual network (ResNet). The system, named ResIdent, is trained only on the data released with the task (closed training). We obtain 84.88% accuracy on subtask A, 68.80% accuracy on subtask B1, and 69.80% accuracy on subtask B2. A large difference in accuracy on development data can be observed with relatively minor changes in our network's architecture and hyperparameters. We therefore expect fine-tuning of these parameters to yield higher accuracies.

Connected through hyperedge HE-1 (induced by source paper "multitask and multilingual modelling for lexical analysis"):
  with members:
    - "parameter sharing between dependency parsers for related languages"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "tracking typological traits of uralic languages in distributed language representations"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "one model to rule them all multitask and multilingual modelling for lexical analysis"
    - "semantic tagging with deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "semantic tagging with deep residual networks"):
  with members:
    - "identity mappings in deep residual networks"
    - "very deep convolutional networks for text classification"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "multi task cross lingual sequence tagging from scratch"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "multilingual language processing from bytes"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)