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: "explainability and adversarial robustness for rnns"
  Abstract: Recurrent Neural Networks (RNNs) yield attractive properties for constructing Intrusion Detection Systems (IDSs) for network data. With the rise of ubiquitous Machine Learning (ML) systems, malicious actors have been catching up quickly to find new ways to exploit ML vulnerabilities for profit. Recently developed adversarial ML techniques focus on computer vision and their applicability to network traffic is not straightforward: Network packets expose fewer features than an image, are sequential and impose several constraints on their features. #R##N#We show that despite these completely different characteristics, adversarial samples can be generated reliably for RNNs. To understand a classifier's potential for misclassification, we extend existing explainability techniques and propose new ones, suitable particularly for sequential data. Applying them shows that already the first packets of a communication flow are of crucial importance and are likely to be targeted by attackers. Feature importance methods show that even relatively unimportant features can be effectively abused to generate adversarial samples. Since traditional evaluation metrics such as accuracy are not sufficient for quantifying the adversarial threat, we propose the Adversarial Robustness Score (ARS) for comparing IDSs, capturing a common notion of adversarial robustness, and show that an adversarial training procedure can significantly and successfully reduce the attack surface.

Connected through hyperedge HE-1 (induced by source paper "sparseids learning packet sampling with reinforcement learning"):
  with members:
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "neural speed reading via skim rnn"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "learning to skim text"

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)