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 "error correcting neural network"):
  Abstract: Error-correcting output codes (ECOC) is an ensemble method combining a set of binary classifiers for multi-class learning problems. However, in traditional ECOC framework, the binary classifiers are trained independently. To explore the interaction between the binary classifiers, we construct an error correction network (ECN) that jointly trains all binary classifiers while maximizing the ensemble diversity to improve its robustness against adversarial attacks. An ECN is built based on a code matrix which is generated by maximizing the error tolerance, i.e., the minimum Hamming distance between any two rows, as well as the ensemble diversity, i.e., the variation of information between any two columns. Though ECN inherently promotes the diversity between the binary classifiers as each ensemble member solves a different classification problem (specified by the corresponding column of the code matrix), we empirically show that the ensemble diversity can be further improved by forcing the weight matrices learned by ensemble members to be orthogonal. The ECN is trained in end-to-end fashion and can be complementary to other defense approaches including adversarial training. We show empirically that ECN is effective against the state-of-the-art while-box attacks while maintaining good accuracy on normal examples.

Includes members:
  Member "a mean field theory of batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "batch normalization biases deep residual networks towards shallow paths")
  Member "improving adversarial robustness via promoting ensemble diversity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "batch normalization is a cause of adversarial vulnerability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks")
  Member "adversarial attacks and defences competition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks")
  Member "defense gan protecting classifiers against adversarial attacks using generative models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "magnet a two pronged defense against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "sequential multi class labeling in crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an unsupervised bayesian neural network for truth discovery")
  Member "defense against adversarial attacks using high level representation guided denoiser":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")

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)