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 compositional neural programs with recursive tree search and planning"):
  Abstract: We propose a novel reinforcement learning algorithm, AlphaNPI, that incorporates the strengths of Neural Programmer-Interpreters (NPI) and AlphaZero. NPI contributes structural biases in the form of modularity, hierarchy and recursion, which are helpful to reduce sample complexity, improve generalization and increase interpretability. AlphaZero contributes powerful neural network guided search algorithms, which we augment with recursion. AlphaNPI only assumes a hierarchical program specification with sparse rewards: 1 when the program execution satisfies the specification, and 0 otherwise. Using this specification, AlphaNPI is able to train NPI models effectively with RL for the first time, completely eliminating the need for strong supervision in the form of execution traces. The experiments show that AlphaNPI can sort as well as previous strongly supervised NPI variants. The AlphaNPI agent is also trained on a Tower of Hanoi puzzle with two disks and is shown to generalize to puzzles with an arbitrary number of disk

Includes members:
  Member "learning to infer program sketches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structural language models of code")
  Member "hierarchical reinforcement learning via advantage weighted information maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multimodal trajectory optimization for motion planning")
  Member "a meta transfer objective for learning to disentangle causal mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "ranked reward enabling self play reinforcement learning for combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning enhanced quantum inspired algorithm for combinatorial optimization")
  Member "hierarchical reinforcement learning with hindsight":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "language as an abstraction for hierarchical deep reinforcement learning")
  Member "near optimal representation learning for hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchically decoupled imitation for morphological transfer")
  Member "improving the universality and learnability of neural programmer interpreters with combinator abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "synthetic datasets for neural program synthesis")
  Member "fast context adaptation via meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta learning for mixed linear regression")

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.AI(Artificial Intelligence)