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: "subregular complexity and deep learning"
  Abstract: This paper argues that the judicial use of formal language theory and grammatical inference are invaluable tools in understanding how deep neural networks can and cannot represent and learn long-term dependencies in temporal sequences. Learning experiments were conducted with two types of Recurrent Neural Networks (RNNs) on six formal languages drawn from the Strictly Local (SL) and Strictly Piecewise (SP) classes. The networks were Simple RNNs (s-RNNs) and Long Short-Term Memory RNNs (LSTMs) of varying sizes. The SL and SP classes are among the simplest in a mathematically well-understood hierarchy of subregular classes. They encode local and long-term dependencies, respectively. The grammatical inference algorithm Regular Positive and Negative Inference (RPNI) provided a baseline. According to earlier research, the LSTM architecture should be capable of learning long-term dependencies and should outperform s-RNNs. The results of these experiments challenge this narrative. First, the LSTMs' performance was generally worse in the SP experiments than in the SL ones. Second, the s-RNNs out-performed the LSTMs on the most complex SP experiment and performed comparably to them on the others.

Connected through hyperedge HE-1 (induced by source paper "tensor product representations of subregular formal languages"):
  with members:
    - "rnns implicitly implement tensor product representations"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "embedding tarskian semantics in vector spaces"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "geometric representations for minimalist grammars"

Connected through hyperedge HE-2 (induced by source paper "multi element long distance dependencies using spk languages to explore the characteristics of long distance dependencies"):
  with members:
    - "using regular languages to explore the representational capacity of recurrent neural architectures"
    - "understanding recurrent neural architectures by analyzing and synthesizing long distance dependencies in benchmark sequential datasets"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-3 (induced by source paper "connecting weighted automata and recurrent neural networks through spectral learning"):
  with members:
    - "rational recurrences"
    - "explaining black boxes on sequential data using weighted automata"
    - "expressive power of recurrent neural networks"
    - "tensor train recurrent neural networks for video classification"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "compressing recurrent neural network with tensor train"
    - "long term forecasting using higher order tensor rnns"
    - "training input output recurrent neural networks through spectral methods"

Connected through hyperedge HE-4 (induced by source paper "understanding recurrent neural architectures by analyzing and synthesizing long distance dependencies in benchmark sequential datasets"):
  with members:
    - "fast parametric learning with activation memorization"
    - "direct output connection for a high rank language model"
    - "frage frequency agnostic word representation"
    - "using regular languages to explore the representational capacity of recurrent neural architectures"
    - "adaptive input representations for neural language modeling"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "dilated recurrent neural networks"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-5 (induced by source paper "using regular languages to explore the representational capacity of recurrent neural architectures"):
  with members:
    - "dilated recurrent neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "recurrent highway networks"
    - "recurrent orthogonal networks and long memory tasks"
    - "pointer sentinel mixture models"
    - "recurrent neural network regularization"

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)