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 learning based approach to reactive security"
  Abstract: Despite the conventional wisdom that proactive security is superior to reactive security, we show that reactive security can be competitive with proactive security as long as the reactive defender learns from past attacks instead of myopically overreacting to the last attack. Our game-theoretic model follows common practice in the security literature by making worst-case assumptions about the attacker: we grant the attacker complete knowledge of the defender's strategy and do not require the attacker to act rationally. In this model, we bound the competitive ratio between a reactive defense algorithm (which is inspired by online learning theory) and the best fixed proactive defense. Additionally, we show that, unlike proactive defenses, this reactive strategy is robust to a lack of information about the attacker's incentives and knowledge.

Connected through hyperedge HE-1 (induced by source paper "wild patterns ten years after the rise of adversarial machine learning"):
  with members:
    - "poisoning behavioral malware clustering"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "is feature selection secure against training data poisoning"
    - "is data clustering in adversarial settings secure"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "certified defenses for data poisoning attacks"
    - "deltaphish detecting phishing webpages in compromised websites"

Connected through hyperedge HE-2 (induced by source paper "adversarial attacks on cognitive self organizing networks the challenge and the way forward"):
  with members:
    - "experience driven networking a deep reinforcement learning based approach"
    - "divide denoise and defend against adversarial attacks"
    - "machine learning for networking workflow advances and opportunities"
    - "deep learning in mobile and wireless networking a survey"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "knowledge defined networking"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "why and how networks should run themselves"

Connected through hyperedge HE-3 (induced by source paper "a dynamic adversarial mining approach to the security of machine learning"):
  with members:
    - "handling adversarial concept drift in streaming data"
    - "sok security and privacy in machine learning"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "security evaluation of pattern classifiers under attack"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "stealing machine learning models via prediction apis"
    - "the limitations of deep learning in adversarial settings"

Connected through hyperedge HE-4 (induced by source paper "large scale strategic games and adversarial machine learning"):
  with members:
    - "random projections for linear support vector machines"

Connected through hyperedge HE-5 (induced by source paper "exact inference techniques for the analysis of bayesian attack graphs"):
  with members:
    - "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"

Connected through hyperedge HE-6 (induced by source paper "security evaluation of support vector machines in adversarial environments"):
  with members:
    - "evasion attacks against machine learning at test time"
    - "calibrating data to sensitivity in private data analysis"
    - "poisoning attacks against support vector machines"
    - "query strategies for evading convex inducing classifiers"
    - "differentially private empirical risk minimization"
    - "learning in a large function space privacy preserving mechanisms for svm learning"

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.CR(Cryptography and Security)