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 offset tree for learning with partial labels"
  Abstract: We present an algorithm, called the Offset Tree, for learning to make decisions in situations where the payoff of only one choice is observed, rather than all choices. The algorithm reduces this setting to binary classification, allowing one to reuse of any existing, fully supervised binary classification algorithm in this partial information setting. We show that the Offset Tree is an optimal reduction to binary classification. In particular, it has regret at most $(k-1)$ times the regret of the binary classifier it uses (where $k$ is the number of choices), and no reduction to binary classification can do better. This reduction is also computationally optimal, both at training and test time, requiring just $O(\log_2 k)$ work to train on an example or make a prediction. #R##N#Experiments with the Offset Tree show that it generally performs better than several alternative approaches.

Connected through hyperedge HE-1 (induced by source paper "neural contextual bandits with ucb based exploration"):
  with members:
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "deep neural linear bandits overcoming catastrophic forgetting through likelihood matching"
    - "on exact computation with an infinitely wide neural net"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "what can resnet learn efficiently going beyond kernels"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "safe exploration for optimizing contextual bandits"):
  with members:
    - "to model or to intervene a comparison of counterfactual and online learning to rank from user interactions"
    - "bubblerank safe online learning to re rank via implicit click feedback"
    - "boltzmann exploration done right"
    - "balancing speed and quality in online learning to rank for information retrieval"
    - "unbiased learning to rank with biased feedback"
    - "risk aware algorithms for adversarial contextual bandits"
    - "off policy evaluation for slate recommendation"
    - "counterfactual risk minimization learning from logged bandit feedback"

Connected through hyperedge HE-3 (induced by source paper "efficient policy learning from surrogate loss classification reductions"):
  with members:
    - "non parametric inference adaptive to intrinsic dimension"
    - "training gans with optimism"
    - "counterfactual risk minimization learning from logged bandit feedback"
    - "doubly robust policy evaluation and learning"

Connected through hyperedge HE-4 (induced by source paper "task selection policies for multitask learning"):
  with members:
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "evaluating the supervised and zero shot performance of multi lingual translation models"
    - "autosem automatic task selection and mixing in multi task learning"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "off the shelf unsupervised nmt"
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "three methods for training on bandit feedback"):
  with members:
    - "recogym a reinforcement learning environment for the problem of product recommendation in online advertising"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "meta learning for contextual bandit exploration"):
  with members:
    - "meta reinforcement learning of structured exploration strategies"
    - "learning to explore with meta policy gradient"
    - "active one shot learning"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"
    - "learning algorithms for active learning"
    - "learning to learn by gradient descent by gradient descent"
    - "deep exploration via bootstrapped dqn"

Connected through hyperedge HE-7 (induced by source paper "melding the data decisions pipeline decision focused learning for combinatorial optimization"):
  with members:
    - "learning approximate inference networks for structured prediction"
    - "optnet differentiable optimization as a layer in neural networks"
    - "task based end to end model learning in stochastic optimization"
    - "learning combinatorial optimization algorithms over graphs"
    - "gradient methods for submodular maximization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"

Connected through hyperedge HE-8 (induced by source paper "adapting multi armed bandits policies to contextual bandits scenarios"):
  with members:
    - "practical contextual bandits with regression oracles"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "thompson sampling with the online bootstrap"
    - "a gang of bandits"
    - "efficient optimal learning for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandits with similarity information"

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)