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: "a formalization of robustness for deep neural networks"
  Abstract: Deep neural networks have been shown to lack robustness to small input perturbations. The process of generating the perturbations that expose the lack of robustness of neural networks is known as adversarial input generation. This process depends on the goals and capabilities of the adversary, In this paper, we propose a unifying formalization of the adversarial input generation process from a formal methods perspective. We provide a definition of robustness that is general enough to capture different formulations. The expressiveness of our formalization is shown by modeling and comparing a variety of adversarial attack techniques.

Connected through hyperedge HE-1 (induced by source paper "towards logical specification of statistical machine learning"):
  with members:
    - "statistical epistemic logic"
    - "adversarial attacks and defences a survey"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "on formalizing fairness in prediction with machine learning"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "synthesizing robust adversarial examples"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-2 (induced by source paper "on the hardness of robust classification"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial vulnerability for any classifier"
    - "adversarial examples from cryptographic pseudo random generators"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "are adversarial examples inevitable"
    - "robustness of classifiers from adversarial to random noise"
    - "analysis of classifiers robustness to adversarial perturbations"
    - "evasion attacks against machine learning at test time"

Connected through hyperedge HE-3 (induced by source paper "testing deep neural networks"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "formal verification of neural network controlled autonomous systems"
    - "symbolic execution for deep neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

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)