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: "the teaching dimension of linear learners"
  Abstract: Teaching dimension is a learning theoretic quantity that specifies the minimum training set size to teach a target model to a learner. Previous studies on teaching dimension focused on version-space learners which maintain all hypotheses consistent with the training data, and cannot be applied to modern machine learners which select a specific hypothesis via optimization. This paper presents the first known teaching dimension for ridge regression, support vector machines, and logistic regression. We also exhibit optimal training sets that match these teaching dimensions. Our approach generalizes to other linear learners.

Connected through hyperedge HE-1 (induced by source paper "learner aware teaching inverse reinforcement learning with preferences and constraints"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "risk aware active inverse reinforcement learning"
    - "an algorithmic perspective on imitation learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "an overview of machine teaching"
    - "teaching categories to human learners with visual explanations"
    - "machine teaching for inverse reinforcement learning algorithms and applications"

Connected through hyperedge HE-2 (induced by source paper "collaborative and privacy preserving machine teaching via consensus optimization"):
  with members:
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "analyzing federated learning through an adversarial lens"
    - "federated learning strategies for improving communication efficiency"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "data poisoning attacks in contextual bandits"
    - "communication efficient distributed dual coordinate ascent"
    - "machine teaching for bayesian learners in the exponential family"
    - "neos server 4 0 administrative guide"

Connected through hyperedge HE-3 (induced by source paper "robust federated training via collaborative machine teaching using trusted instances"):
  with members:
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "analyzing federated learning through an adversarial lens"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "training set debugging using trusted items"
    - "machine teaching for bayesian learners in the exponential family"
    - "identifying mislabeled training data"

Connected through hyperedge HE-4 (induced by source paper "learning a manifold from a teacher s demonstrations"):
  with members:
    - "a unified framework for manifold landmarking"
    - "deep learning with topological signatures"

Connected through hyperedge HE-5 (induced by source paper "machine teaching for inverse reinforcement learning algorithms and applications"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "an overview of machine teaching"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "enabling robots to communicate their objectives"
    - "efficient probabilistic performance bounds for inverse reinforcement learning"
    - "preference based teaching"
    - "cooperative inverse reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "learning to teach"):
  with members:
    - "automated curriculum learning for neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"
    - "on graduated optimization for stochastic non convex problems"
    - "semi supervised sequence learning"

Connected through hyperedge HE-7 (induced by source paper "understanding the role of adaptivity in machine teaching the case of version space learners"):
  with members:
    - "an overview of machine teaching"
    - "preference based teaching"
    - "becoming the expert interactive multi class machine teaching"
    - "a theory of formal synthesis via inductive learning"
    - "discrete stochastic submodular maximization adaptive vs non adaptive vs offline"
    - "machine teaching for bayesian learners in the exponential family"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "training set camouflage"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "training set debugging using trusted items"
    - "randomized prediction games for adversarial machine learning"
    - "deep residual learning for image recognition"
    - "efficient estimation of word representations in vector space"

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)