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: "connecting weighted automata and recurrent neural networks through spectral learning"
  Abstract: In this paper, we unravel a fundamental connection between weighted finite automata~(WFAs) and second-order recurrent neural networks~(2-RNNs): in the case of sequences of discrete symbols, WFAs and 2-RNNs with linear activation functions are expressively equivalent. Motivated by this result, we build upon a recent extension of the spectral learning algorithm to vector-valued WFAs and propose the first provable learning algorithm for linear 2-RNNs defined over sequences of continuous input vectors. This algorithm relies on estimating low rank sub-blocks of the so-called Hankel tensor, from which the parameters of a linear 2-RNN can be provably recovered. The performances of the proposed method are assessed in a simulation study.

Connected through hyperedge HE-1 (induced by source paper "tensor networks for language modeling"):
  with members:
    - "singular value automata and approximate minimization"
    - "attention is all you need"
    - "controlling linguistic style aspects in neural language generation"
    - "language as a matrix product state"
    - "regularizing and optimizing lstm language models"
    - "tensor network language model"
    - "language design as information renormalization"
    - "parallelizing linear recurrent neural nets over sequence length"

Connected through hyperedge HE-2 (induced by source paper "weighted automata extraction from recurrent neural networks via regression on state spaces"):
  with members:
    - "state regularized recurrent neural networks"
    - "intrusion prediction with system call sequence to sequence model"
    - "explaining black boxes on sequential data using weighted automata"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "bisimulation metrics for weighted automata"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "advances in all neural speech recognition"

Connected through hyperedge HE-3 (induced by source paper "learning deterministic weighted automata with queries and counterexamples"):
  with members:
    - "weighted automata extraction from recurrent neural networks via regression on state spaces"
    - "explaining black boxes on sequential data using weighted automata"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "a maximum matching algorithm for basis selection in spectral learning"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "prediction with a short memory"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-4 (induced by source paper "representing formal languages a comparison between finite automata and recurrent neural networks"):
  with members:
    - "explaining black boxes on sequential data using weighted automata"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-5 (induced by source paper "connecting first and second order recurrent networks with deterministic finite automata"):
  with members:
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "on multiplicative integration with recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"

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)