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 "endowing robots with longer term autonomy by recovering from external disturbances in manipulation through grounded anomaly classification and recovery policies"):
  Abstract: Robot manipulation is increasingly poised to interact with humans in co-shared workspaces. Despite increasingly robust manipulation and control algorithms, failure modes continue to exist whenever models do not capture the dynamics of the unstructured environment. To obtain longer-term horizons in robot automation, robots must develop introspection and recovery abilities. We contribute a set of recovery policies to deal with anomalies produced by external disturbances as well as anomaly classification through the use of non-parametric statistics with memoized variational inference with scalable adaptation. A recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system that resolves a wide range of anomalous situations. Policies, skills, and introspection models are learned incrementally and contextually in a task. Two task-level recovery policies: re-enactment and adaptation resolve accidental and persistent anomalies respectively. The introspection system uses non-parametric priors along with Markov jump linear systems and memoized variational inference with scalable adaptation to learn a model from the data. Extensive real-robot experimentation with various strenuous anomalous conditions is induced and resolved at different phases of a task and in different combinations. The system executes around-the-clock introspection and recovery and even elicited self-recovery when misclassifications occurred.

Includes members:
  Member "composable deep reinforcement learning for robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving generalization of reinforcement learning with minimax distributional soft actor critic")
  Member "identifying object states in cooking related images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey of knowledge representation in service robotics")
  Member "learning human robot collaboration insights through the integration of muscle activity in interaction motion models":
    also connected through other hyperedges:
      (none)
  Member "online robot introspection via wrench based action grammars":
    also connected through other hyperedges:
      (none)
  Member "robot introspection with bayesian nonparametric vector autoregressive hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast robust and versatile event detection through hmm belief state gradient measures")
  Member "fast robust and versatile event detection through hmm belief state gradient measures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "what went wrong identification of everyday object manipulation anomalies")
  Member "functional object oriented network for manipulation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey of knowledge representation in service robotics")
  Member "a probabilistic representation for dynamic movement primitives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural probabilistic motor primitives for humanoid control")

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.RO(Robotics)