human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "towards an efficient and general framework of robust training for graph neural networks"):
  Abstract: Graph Neural Networks (GNNs) have made significant advances on several fundamental inference tasks. As a result, there is a surge of interest in using these models for making potentially important decisions in high-regret applications. However, despite GNNs' impressive performance, it has been observed that carefully crafted perturbations on graph structures (or nodes attributes) lead them to make wrong predictions. Presence of these adversarial examples raises serious security concerns. Most of the existing robust GNN design/training methods are only applicable to white-box settings where model parameters are known and gradient based methods can be used by performing convex relaxation of the discrete graph domain. More importantly, these methods are not efficient and scalable which make them infeasible in time sensitive tasks and massive graph datasets. To overcome these limitations, we propose a general framework which leverages the greedy search algorithms and zeroth-order methods to obtain robust GNNs in a generic and an efficient manner. On several applications, we show that the proposed techniques are significantly less computationally expensive and, in some cases, more robust than the state-of-the-art methods making them suitable to large-scale problems which were out of the reach of traditional robust training methods.

Includes members:
  Member "topology attack and defense for graph neural networks an optimization perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent")
  Member "zo adamm zeroth order adaptive momentum method for black box optimization":
    also connected through other hyperedges:
      (none)
  Member "adversarial attacks on graph neural networks via meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "certifiable robustness and robust training for graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "interpreting adversarial examples by activation promotion and suppression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "heat and blur an effective and fast defense against adversarial examples")
  Member "adversarial attack on graph structured data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "zeroth order stochastic variance reduction for nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "man in the middle attacks against machine learning classifiers via malicious generative models")

Question: Please tell me which class the source paper 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)