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 "manga method agnostic neural policy generalization and adaptation"):
  Abstract: In this paper we target the problem of transferring policies across multiple environments with different dynamics parameters and motor noise variations, by introducing a framework that decouples the processes of policy learning and system identification. Efficiently transferring learned policies to an unknown environment with changes in dynamics configurations in the presence of motor noise is very important for operating robots in the real world, and our work is a novel attempt in that direction. We introduce MANGA: Method Agnostic Neural-policy Generalization and Adaptation, that trains dynamics conditioned policies and efficiently learns to estimate the dynamics parameters of the environment given off-policy state-transition rollouts in the environment. Our scheme is agnostic to the type of training method used - both reinforcement learning (RL) and imitation learning (IL) strategies can be used. We demonstrate the effectiveness of our approach by experimenting with four different MuJoCo agents and comparing against previously proposed transfer baselines.

Includes members:
  Member "learning to augment synthetic images for sim2real policy transfer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to combine primitive skills a step towards versatile robotic manipulation")
  Member "sim to real transfer for biped locomotion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning fast adaptation with meta strategy optimization")
  Member "norml no reward meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "sim real joint reinforcement transfer for 3d indoor navigation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning based visual navigation with information theoretic regularization")
  Member "splitnet sim2sim and task2task transfer for embodied visual navigation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "audio visual embodied navigation")
  Member "exploration by random network distillation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning stable deep dynamics models")
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "never give up learning directed exploration strategies")

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)