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: "using regular languages to explore the representational capacity of recurrent neural architectures"
  Abstract: The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. The degree of LDDs within the generated data can be controlled through the k parameter, length of the generated strings, and by choosing appropriate forbidden strings. In this paper, we explore the capacity of different RNN extensions to model LDDs, by evaluating these models on a sequence of SPk synthesized datasets, where each subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple languages, the presence of LDDs does have significant impact on the performance of recurrent neural architectures, thus making them prime candidate in benchmarking tasks.

Connected through hyperedge HE-1 (induced by source paper "semantic relatedness and taxonomic word embeddings"):
  with members:
    - "persistence pays off paying attention to what the lstm gating mechanism persists"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "understanding recurrent neural architectures by analyzing and synthesizing long distance dependencies in benchmark sequential datasets"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

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:
    - "understanding recurrent neural architectures by analyzing and synthesizing long distance dependencies in benchmark sequential datasets"
    - "regularizing and optimizing lstm language models"
    - "subregular complexity and deep learning"

Connected through hyperedge HE-3 (induced by source paper "referring to the recently seen reference and perceptual memory in situated dialog"):
  with members:
    - "grounded semantic composition for visual scenes"
    - "the symbol grounding problem"

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"
    - "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"
    - "subregular complexity and deep learning"

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.LG(Machine Learning)