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: "response characterization for auditing cell dynamics in long short term memory networks"
  Abstract: In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level. We propose a systematic pipeline for interpreting individual hidden state dynamics within the network using response characterization methods. The ranked contribution of individual cells to the network's output is computed by analyzing a set of interpretable metrics of their decoupled step and sinusoidal responses. As a result, our method is able to uniquely identify neurons with insightful dynamics, quantify relationships between dynamical properties and test accuracy through ablation analysis, and interpret the impact of network capacity on a network's dynamical distribution. Finally, we demonstrate generalizability and scalability of our method by evaluating a series of different benchmark sequential datasets.

Connected through hyperedge HE-1 (induced by source paper "input cell attention reduces vanishing saliency of recurrent neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "exploring interpretable lstm neural networks over multi variable data"
    - "interpretable lstms for whole brain neuroimaging analyses"
    - "analyzing neuroimaging data through recurrent deep learning models"
    - "methods for interpreting and understanding deep neural networks"
    - "smoothgrad removing noise by adding noise"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "can a compact neuronal circuit policy be re purposed to learn simple robotic control"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "liquid time constant recurrent neural networks as universal approximators"
    - "proximal policy optimization algorithms"
    - "benchmarking deep reinforcement learning for continuous control"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-3 (induced by source paper "re purposing compact neuronal circuit policies to govern reinforcement learning tasks"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "liquid time constant recurrent neural networks as universal approximators"
    - "proximal policy optimization algorithms"
    - "benchmarking deep reinforcement learning for continuous control"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "visualizing and understanding recurrent networks"

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)