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 "sparse graphical memory for robust planning"):
  Abstract: To operate effectively in the real world, artificial agents must act from raw sensory input such as images and achieve diverse goals across long time-horizons. On the one hand, recent strides in deep reinforcement and imitation learning have demonstrated impressive ability to learn goal-conditioned policies from high-dimensional image input, though only for short-horizon tasks. On the other hand, classical graphical methods like A* search are able to solve long-horizon tasks, but assume that the graph structure is abstracted away from raw sensory input and can only be constructed with task-specific priors. We wish to combine the strengths of deep learning and classical planning to solve long-horizon tasks from raw sensory input. To this end, we introduce Sparse Graphical Memory (SGM), a new data structure that stores observations and feasible transitions in a sparse memory. SGM can be combined with goal-conditioned RL or imitative agents to solve long-horizon tasks across a diverse set of domains. We show that SGM significantly outperforms current state of the art methods on long-horizon, sparse-reward visual navigation tasks. Project video and code are available at this https URL

Includes members:
  Member "learning world graphs to accelerate hierarchical reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "search on the replay buffer bridging planning and reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement")
  Member "benchmarking classic and learned navigation in complex 3d environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an exploration of embodied visual exploration")
  Member "mapping state space using landmarks for universal goal reaching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scoring aggregating planning learning task agnostic priors from interactions and sparse rewards for zero shot generalization")
  Member "hallucinative topological memory for zero shot visual planning":
    also connected through other hyperedges:
      (none)
  Member "temporal difference models model free deep rl for model based control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement")
  Member "visual reinforcement learning with imagined goals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty")
  Member "distributed distributional deterministic policy gradients":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")

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)