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: "an optimal control view of adversarial machine learning"
  Abstract: I describe an optimal control view of adversarial machine learning, where the dynamical system is the machine learner, the input are adversarial actions, and the control costs are defined by the adversary's goals to do harm and be hard to detect. This view encompasses many types of adversarial machine learning, including test-item attacks, training-data poisoning, and adversarial reward shaping. The view encourages adversarial machine learning researcher to utilize advances in control theory and reinforcement learning.

Connected through hyperedge HE-1 (induced by source paper "online data poisoning attack"):
  with members:
    - "optimal adversarial attack on autoregressive models"
    - "online control with adversarial disturbances"
    - "training set camouflage"
    - "an optimal control approach to sequential machine teaching"
    - "online linear quadratic control"
    - "data poisoning attacks in contextual bandits"
    - "data poisoning attacks against online learning"
    - "towards poisoning of deep learning algorithms with back gradient optimization"

Connected through hyperedge HE-2 (induced by source paper "preference based batch and sequential teaching towards a unified view of models"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "learner aware teaching inverse reinforcement learning with preferences and constraints"
    - "optimal collusion free teaching"
    - "an optimal control approach to sequential machine teaching"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "quadratic upper bound for recursive teaching dimension of finite vc classes"
    - "near optimally teaching the crowd to classify"

Connected through hyperedge HE-3 (induced by source paper "optimal adversarial attack on autoregressive models"):
  with members:
    - "data poisoning against differentially private learners attacks and defenses"
    - "online data poisoning attack"
    - "an optimal control approach to sequential machine teaching"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "data poisoning attacks in contextual bandits"
    - "data poisoning attacks against online learning"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "security evaluation of support vector machines in adversarial environments"

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)