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: "models and framework for adversarial attacks on complex adaptive systems"
  Abstract: We introduce the paradigm of adversarial attacks that target the dynamics of Complex Adaptive Systems (CAS). To facilitate the analysis of such attacks, we present multiple approaches to the modeling of CAS as dynamical, data-driven, and game-theoretic systems, and develop quantitative definitions of attack, vulnerability, and resilience in the context of CAS security. Furthermore, we propose a comprehensive set of schemes for classification of attacks and attack surfaces in CAS, complemented with examples of practical attacks. Building on this foundation, we propose a framework based on reinforcement learning for simulation and analysis of attacks on CAS, and demonstrate its performance through three real-world case studies of targeting power grids, destabilization of terrorist organizations, and manipulation of machine learning agents. We also discuss potential mitigation techniques, and remark on future research directions in analysis and design of secure complex adaptive systems.

Connected through hyperedge HE-1 (induced by source paper "multiparty dynamics and failure modes for machine learning and artificial intelligence"):
  with members:
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "oversight of unsafe systems via dynamic safety envelopes"
    - "how do classifiers induce agents to invest effort strategically"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "supervising strong learners by amplifying weak experts"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "robustness to fundamental uncertainty in agi alignment"
    - "data poisoning attacks against online learning"

Connected through hyperedge HE-2 (induced by source paper "overoptimization failures and specification gaming in multi agent systems"):
  with members:
    - "oversight of unsafe systems via dynamic safety envelopes"
    - "supervising strong learners by amplifying weak experts"
    - "categorizing variants of goodhart s law"
    - "data poisoning attacks against online learning"
    - "on a formal model of safe and scalable self driving cars"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "concrete problems in ai safety"
    - "stealing machine learning models via prediction apis"

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.SY(Systems and Control)