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 "plan arithmetic compositional plan vectors for multi task control"):
  Abstract: Autonomous agents situated in real-world environments must be able to master large repertoires of skills. While a single short skill can be learned quickly, it would be impractical to learn every task independently. Instead, the agent should share knowledge across behaviors such that each task can be learned efficiently, and such that the resulting model can generalize to new tasks, especially ones that are compositions or subsets of tasks seen previously. A policy conditioned on a goal or demonstration has the potential to share knowledge between tasks if it sees enough diversity of inputs. However, these methods may not generalize to a more complex task at test time. We introduce compositional plan vectors (CPVs) to enable a policy to perform compositions of tasks without additional supervision. CPVs represent trajectories as the sum of the subtasks within them. We show that CPVs can be learned within a one-shot imitation learning framework without any additional supervision or information about task hierarchy, and enable a demonstration-conditioned policy to generalize to tasks that sequence twice as many skills as the tasks seen during training. #R##N#Analogously to embeddings such as word2vec in NLP, CPVs can also support simple arithmetic operations -- for example, we can add the CPVs for two different tasks to command an agent to compose both tasks, without any additional training.

Includes members:
  Member "hierarchical visuomotor control of humanoids":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural embedding for physical manipulations")
  Member "composable deep reinforcement learning for robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving generalization of reinforcement learning with minimax distributional soft actor critic")
  Member "attributes as operators factorizing unseen attribute object compositions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to learn words from visual scenes")
  Member "hierarchical reinforcement learning for zero shot generalization with subtask dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta reinforcement learning with autonomous inference of subtask dependencies")
  Member "latent space policies for hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning for combinatorial optimization a survey")
  Member "task embedded control networks for few shot imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "goal conditioned end to end visuomotor control for versatile skill primitives")
  Member "neural probabilistic motor primitives for humanoid control":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "product kanerva machines factorized bayesian memory")
  Member "multi modal imitation learning from unstructured demonstrations using generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "machine learning approaches for motor learning a short review")

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)