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 approach to sequential machine teaching"
  Abstract: Given a sequential learning algorithm and a target model, sequential machine teaching aims to find the shortest training sequence to drive the learning algorithm to the target model. We present the first principled way to find such shortest training sequences. Our key insight is to formulate sequential machine teaching as a time-optimal control problem. This allows us to solve sequential teaching by leveraging key theoretical and computational tools developed over the past 60 years in the optimal control community. Specifically, we study the Pontryagin Maximum Principle, which yields a necessary condition for optimality of a training sequence. We present analytic, structural, and numerical implications of this approach on a case study with a least-squares loss function and gradient descent learner. We compute optimal training sequences for this problem, and although the sequences seem circuitous, we find that they can vastly outperform the best available heuristics for generating training sequences.

Connected through hyperedge HE-1 (induced by source paper "nloptcontrol a modeling language for solving optimal control problems"):
  with members:
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "julia a fast dynamic language for technical computing"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-2 (induced by source paper "online data poisoning attack"):
  with members:
    - "optimal adversarial attack on autoregressive models"
    - "online control with adversarial disturbances"
    - "training set camouflage"
    - "online linear quadratic control"
    - "an optimal control view of adversarial machine learning"
    - "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-3 (induced by source paper "an investigation of data poisoning defenses for online learning"):
  with members:
    - "online data poisoning attack"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "sever a robust meta algorithm for stochastic optimization"
    - "data poisoning attacks in contextual bandits"
    - "data poisoning attacks against online learning"
    - "certified defenses for data poisoning attacks"
    - "being robust in high dimensions can be practical"
    - "learning under p tampering attacks"

Connected through hyperedge HE-4 (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"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "an optimal control view of adversarial machine learning"
    - "quadratic upper bound for recursive teaching dimension of finite vc classes"
    - "near optimally teaching the crowd to classify"

Connected through hyperedge HE-5 (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"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "an optimal control view 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"

Connected through hyperedge HE-6 (induced by source paper "optimal attack against autoregressive models by manipulating the environment"):
  with members:
    - "data poisoning attacks in contextual bandits"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-7 (induced by source paper "barrier certificates for assured machine teaching"):
  with members:
    - "verification of uncertain pomdps using barrier certificates"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "privacy verification in pomdps via barrier certificates"
    - "machine teaching a new paradigm for building machine learning systems"
    - "preference based teaching"

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)