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: "pots protective optimization technologies"
  Abstract: In spite of their many advantages, optimization systems often neglect the economic, ethical, moral, social, and political impact they have on populations and their environments. In this paper we argue that the frameworks through which the discontents of optimization systems have been approached so far cover a narrow subset of these problems by (i) assuming that the system provider has the incentives and means to mitigate the imbalances optimization causes, (ii) disregarding problems that go beyond discrimination due to disparate treatment or impact in algorithmic decision making, and (iii) developing solutions focused on removing algorithmic biases related to discrimination. #R##N#In response we introduce Protective Optimization Technologies: solutions that enable optimization subjects to defend from unwanted consequences. We provide a framework that formalizes the design space of POTs and show how it differs from other design paradigms in the literature. We show how the framework can capture strategies developed in the wild against real optimization systems, and how it can be used to design, implement, and evaluate a POT that enables individuals and collectives to protect themselves from unbalances in a credit scoring application related to loan allocation.

Connected through hyperedge HE-1 (induced by source paper "evading classifiers in discrete domains with provable optimality guarantees"):
  with members:
    - "scaling provable adversarial defenses"
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "cost sensitive robustness against adversarial examples"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "generating natural language adversarial examples"

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.CY(Computers and Society)