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 "adversarial examples for models of code"):
  Abstract: Neural models of code have shown impressive performance for tasks such as predicting method names and identifying certain kinds of bugs. In this paper, we show that these models are vulnerable to adversarial examples, and introduce a novel approach for attacking trained models of code with adversarial examples. The main idea is to force a given trained model to make an incorrect prediction as specified by the adversary by introducing small perturbations that do not change the program's semantics. To find such perturbations, we present a new technique for Discrete Adversarial Manipulation of Programs (DAMP). DAMP works by deriving the desired prediction with respect to the model's inputs while holding the model weights constant and following the gradients to slightly modify the code. #R##N#To defend a model against such attacks, we propose placing a defensive model (Anti-DAMP) in front of it. Anti-DAMP detects unlikely mutations and masks them before feeding the input to the downstream model. #R##N#We show that our DAMP attack is effective across three neural architectures: code2vec, GGNN, and GNN-FiLM, in both Java and C#. We show that DAMP has up to 89% success rate in changing a prediction to the adversary's choice ("targeted attack"), and a success rate of up to 94% in changing a given prediction to any incorrect prediction ("non-targeted attack"). By using Anti-DAMP, the success rate of the attack drops drastically for both targeted and non-targeted attacks, with a minor penalty of 2% relative degradation in accuracy while not performing under attack.

Includes members:
  Member "combating adversarial misspellings with robust word recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "gnn film graph neural networks with feature wise linear modulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benchmarking graph neural networks")
  Member "when deep learning met code search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "searching a database of source codes using contextualized code search")
  Member "getafix learning to fix bugs automatically":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to fix build errors with graph2diff neural networks")
  Member "structural language models for any code generation":
    also connected through other hyperedges:
      (none)
  Member "code2vec learning distributed representations of code":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial robustness for code")
  Member "targeted adversarial examples for black box audio systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "g odel s sentence is an adversarial example but unsolvable")
  Member "a general path based representation for predicting program properties":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structural language models of code")

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)