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 "gradient based adversarial attacks on categorical sequence models via traversing an embedded world"):
  Abstract: An adversarial attack paradigm explores various scenarios for vulnerability of machine and especially deep learning models: we can apply minor changes to the model input to force a classifier's failure for a particular example. Most of the state of the art frameworks focus on adversarial attacks for images and other structured model inputs. The adversarial attacks for categorical sequences can also be harmful if they are successful. However, successful attacks for inputs based on categorical sequences should address the following challenges: (1) non-differentiability of the target function, (2) constraints on transformations of initial sequences, and (3) diversity of possible problems. We handle these challenges using two approaches. The first approach adopts Monte-Carlo methods and allows usage in any scenario, the second approach uses a continuous relaxation of models and target metrics, and thus allows using general state of the art methods on adversarial attacks with little additional effort. Results for money transactions, medical fraud, and NLP datasets suggest the proposed methods generate reasonable adversarial sequences that are close to original ones, but fool machine learning models even for blackbox adversarial attacks.

Includes members:
  Member "towards a human like open domain chatbot":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation")
  Member "cyclical stochastic gradient mcmc for bayesian deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the case for bayesian deep learning")
  Member "mass masked sequence to sequence pre training for language generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "sequence embeddings help to identify fraudulent cases in healthcare insurance":
    also connected through other hyperedges:
      (none)
  Member "soft edit distance for differentiable comparison of symbolic sequences":
    also connected through other hyperedges:
      (none)
  Member "is bert really robust a strong baseline for natural language attack on text classification and entailment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "adversarial attacks on deep learning models in natural language processing a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "g odel s sentence is an adversarial example but unsolvable")
  Member "black box generation of adversarial text sequences to evade deep learning classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")

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)