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: "an empirical exploration of skip connections for sequential tagging"
  Abstract: In this paper, we empirically explore the effects of various kinds of skip connections in stacked bidirectional LSTMs for sequential tagging. We investigate three kinds of skip connections connecting to LSTM cells: (a) skip connections to the gates, (b) skip connections to the internal states and (c) skip connections to the cell outputs. We present comprehensive experiments showing that skip connections to cell outputs outperform the remaining two. Furthermore, we observe that using gated identity functions as skip mappings works pretty well. Based on this novel skip connections, we successfully train deep stacked bidirectional LSTM models and obtain state-of-the-art results on CCG supertagging and comparable results on POS tagging.

Connected through hyperedge HE-1 (induced by source paper "simple recurrent units for highly parallelizable recurrence"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "rational recurrences"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "an analysis of neural language modeling at multiple scales"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deriving neural architectures from sequence and graph kernels"
    - "lstm a search space odyssey"
    - "recurrent additive networks"

Connected through hyperedge HE-2 (induced by source paper "shortcut sequence tagging"):
  with members:
    - "deep networks with stochastic depth"
    - "recurrent highway networks"
    - "a dynamic window neural network for ccg supertagging"
    - "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-3 (induced by source paper "syntax aware lstm model for chinese semantic role labeling"):
  with members:
    - "neural semantic role labeling with dependency path embeddings"

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)