human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning neural parsers with deterministic differentiable imitation learning"):
  Abstract: We explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured partitions of their input spaces, we formulate the problem of decomposing objects into segments as a parsing approach. We make the insight that the derivation of a parse-tree that decomposes the object into segments closely resembles a decision tree constructed by ID3, which can be done when the ground-truth available. We learn to imitate an expert parsing oracle, such that our neural parser can generalize to parse natural images without ground truth. We introduce a novel deterministic policy gradient update, DRAG (i.e., DeteRministically AGgrevate) in the form of a deterministic actor-critic variant of AggreVaTeD, to train our neural parser. From another perspective, our approach is a variant of the Deterministic Policy Gradient suitable for the imitation learning setting. The deterministic policy representation offered by training our neural parser with DRAG allows it to outperform state of the art imitation and reinforcement learning approaches.

Includes members:
  Member "adaptive information gathering via imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mpc net a first principles guided policy search")
  Member "deeply aggrevated differentiable imitation learning for sequential prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploration efficient deep reinforcement learning with demonstration guidance for robot control")
  Member "learning heuristic search via imitation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners")
  Member "a review on deep learning techniques applied to semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf")
  Member "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mix and match networks multi domain alignment for unpaired image to image translation")
  Member "continuous control with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "reinforcement and imitation learning via interactive no regret learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning hierarchical teaching policies for cooperative agents")
  Member "off policy actor critic":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")

Question: Please tell me which class the source paper 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)