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: "explaining black boxes on sequential data using weighted automata"
  Abstract: Understanding how a learned black box works is of crucial interest for the future of Machine Learning. In this paper, we pioneer the question of the global interpretability of learned black box models that assign numerical values to symbolic sequential data. To tackle that task, we propose a spectral algorithm for the extraction of weighted automata (WA) from such black boxes. This algorithm does not require the access to a dataset or to the inner representation of the black box: the inferred model can be obtained solely by querying the black box, feeding it with inputs and analyzing its outputs. Experiments using Recurrent Neural Networks (RNN) trained on a wide collection of 48 synthetic datasets and 2 real datasets show that the obtained approximation is of great quality.

Connected through hyperedge HE-1 (induced by source paper "analyzing recurrent neural network by probabilistic abstraction"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "learning with interpretable structure from rnn"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "safety and trustworthiness of deep neural networks a survey"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"

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"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "connecting weighted automata and recurrent neural networks through spectral learning"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "connecting weighted automata and recurrent neural networks through spectral learning"
    - "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 "knowledge extraction from the learning of sequences in a long short term memory lstm architecture"):
  with members:
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "lstm a search space odyssey"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "the mythos of model interpretability"
    - "representation learning a review and new perspectives"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-5 (induced by source paper "representing formal languages a comparison between finite automata and recurrent neural networks"):
  with members:
    - "connecting weighted automata and recurrent neural networks through spectral learning"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-6 (induced by source paper "connecting weighted automata and recurrent neural networks through spectral learning"):
  with members:
    - "rational recurrences"
    - "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"
    - "subregular complexity and deep learning"
    - "long term forecasting using higher order tensor rnns"
    - "training input output recurrent neural networks through spectral methods"

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)