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: "universal transferable and targeted adversarial attacks"
  Abstract: Deep Neural Network has been found vulnerable recently. A kind of well-designed inputs, which called adversarial examples, can lead the networks to make incorrect predictions. Depending on the different scenarios, goals and capabilities, the difficulty to generate the attack is different. For example, generating a targeted attack is more difficult than a non-targeted attack, a universal attack is more difficult than a non-universal attack, a transferable attack is more difficult than a nontransferable one. The question is: Is there exist an attack that can survival in the most harsh adversity to meet all these requirements. Although many cheap and effective attacks have been proposed, this question is still not completely solved over large models and large scale dataset. In this paper, we learn a universal mapping from the sources to the adversarial examples. These examples can fool classification networks into classifying all of them to one targeted class. Besides, they are also transferable between different models.

Connected through hyperedge HE-1 (induced by source paper "graph universal adversarial attacks a few bad actors ruin graph learning models"):
  with members:
    - "session based recommendation with graph neural network"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial attacks on graph neural networks via meta learning"
    - "adversarial attack on graph structured data"
    - "adversarial patch"
    - "attack graph convolutional networks by adding fake nodes"
    - "neural message passing for quantum chemistry"

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)