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: "learning policies for contextual submodular prediction"
  Abstract: Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options. Such lists are often evaluated using submodular reward functions that measure both quality and diversity. We propose a simple, efficient, and provably near-optimal approach to optimizing such prediction problems based on no-regret learning. Our method leverages a surprising result from online submodular optimization: a single no-regret online learner can compete with an optimal sequence of predictions. Compared to previous work, which either learn a sequence of classifiers or rely on stronger assumptions such as realizability, we ensure both data-efficiency as well as performance guarantees in the fully agnostic setting. Experiments validate the efficiency and applicability of the approach on a wide range of problems including manipulator trajectory optimization, news recommendation and document summarization.

Connected through hyperedge HE-1 (induced by source paper "deeply aggrevated differentiable imitation learning for sequential prediction"):
  with members:
    - "adaptive information gathering via imitation learning"
    - "deep reinforcement learning for dialogue generation"
    - "generative adversarial imitation learning"
    - "an actor critic algorithm for sequence prediction"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "plato policy learning using adaptive trajectory optimization"
    - "benchmarking deep reinforcement learning for continuous control"
    - "sequence level training with recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "analysis and optimization of loss functions for multiclass top k and multilabel classification"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "cnn rnn a unified framework for multi label image classification"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "deep residual learning for image recognition"
    - "places205 vggnet models for scene recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-3 (induced by source paper "learning preferences for manipulation tasks from online coactive feedback"):
  with members:
    - "deep learning for detecting robotic grasps"
    - "learning trajectory preferences for manipulators via iterative improvement"
    - "continuous inverse optimal control with locally optimal examples"
    - "online structured prediction via coactive learning"
    - "learning to place new objects in a scene"
    - "sampling based algorithms for optimal motion planning"
    - "incremental sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "reinforcement and imitation learning via interactive no regret learning"):
  with members:
    - "approximate policy iteration schemes a comparison"
    - "the interplay between stability and regret in online learning"
    - "decoupling exploration and exploitation in multi armed bandits"
    - "projection free online learning"
    - "stability conditions for online learnability"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-5 (induced by source paper "visual chunking a list prediction framework for region based object detection"):
  with members:
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "search based structured prediction"

Connected through hyperedge HE-6 (induced by source paper "online submodular maximization under a matroid constraint with application to learning assignments"):
  with members:
    - "online learning of assignments that maximize submodular functions"
    - "sponsored search auctions with markovian users"
    - "algorithmic methods for sponsored search advertising"

Connected through hyperedge HE-7 (induced by source paper "knapsack constrained contextual submodular list prediction with application to multi document summarization"):
  with members:
    - "agnostic system identification for model based reinforcement learning"
    - "learning determinantal point processes"
    - "predicting contextual sequences via submodular function maximization"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

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)