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: "machine teaching for bayesian learners in the exponential family"
  Abstract: What if there is a teacher who knows the learning goal and wants to design good training data for a machine learner? We propose an optimal teaching framework aimed at learners who employ Bayesian models. Our framework is expressed as an optimization problem over teaching examples that balance the future loss of the learner and the effort of the teacher. This optimization problem is in general hard. In the case where the learner employs conjugate exponential family models, we present an approximate algorithm for finding the optimal teaching set. Our algorithm optimizes the aggregate sufficient statistics, then unpacks them into actual teaching examples. We give several examples to illustrate our framework.

Connected through hyperedge HE-1 (induced by source paper "dataset distillation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "few shot adversarial domain adaptation"
    - "data distillation towards omni supervised learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "data free knowledge distillation for deep neural networks"
    - "data poisoning attacks on factorization based collaborative filtering"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences"):
  with members:
    - "transfer of temporal logic formulas in reinforcement learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "teaching categories to human learners with visual explanations"
    - "learning linear temporal properties"
    - "machine teaching for inverse reinforcement learning algorithms and applications"

Connected through hyperedge HE-3 (induced by source paper "sequential cooperative bayesian inference"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "a mathematical theory of cooperative communication"
    - "generalizing the theory of cooperative inference"
    - "optimal cooperative inference"
    - "cooperative inverse reinforcement learning"
    - "infant directed speech is consistent with teaching"

Connected through hyperedge HE-4 (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-5 (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"
    - "an optimal control view of adversarial machine learning"
    - "quadratic upper bound for recursive teaching dimension of finite vc classes"

Connected through hyperedge HE-6 (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"
    - "the teaching dimension of linear learners"
    - "communication efficient distributed dual coordinate ascent"
    - "neos server 4 0 administrative guide"

Connected through hyperedge HE-7 (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"
    - "the teaching dimension of linear learners"
    - "identifying mislabeled training data"

Connected through hyperedge HE-8 (induced by source paper "class teaching for inverse reinforcement learners"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "an overview of machine teaching"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "multi armed bandits for intelligent tutoring systems"

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)