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 dynamic window neural network for ccg supertagging"
  Abstract: Combinatory Category Grammar (CCG) supertagging is a task to assign lexical categories to each word in a sentence. Almost all previous methods use fixed context window sizes as input features. However, it is obvious that different tags usually rely on different context window sizes. These motivate us to build a supertagger with a dynamic window approach, which can be treated as an attention mechanism on the local contexts. Applying dropout on the dynamic filters can be seen as drop on words directly, which is superior to the regular dropout on word embeddings. We use this approach to demonstrate the state-of-the-art CCG supertagging performance on the standard test set.

Connected through hyperedge HE-1 (induced by source paper "shortcut sequence tagging"):
  with members:
    - "deep networks with stochastic depth"
    - "recurrent highway networks"
    - "an empirical exploration of skip connections for sequential tagging"
    - "neural architectures for named entity recognition"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "hierarchical multiscale recurrent neural networks"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "an empirical exploration of skip connections for sequential tagging"):
  with members:
    - "recurrent highway networks"
    - "neural architectures for named entity recognition"
    - "theano a python framework for fast computation of mathematical expressions"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"
    - "deep residual learning for image recognition"
    - "grid long short term memory"
    - "highway long short term memory rnns for distant speech recognition"

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)