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 "what went wrong identification of everyday object manipulation anomalies"):
  Abstract: Extending the abilities of service robots is important for expanding what they can achieve in everyday manipulation tasks. On the other hand, it is also essential to ensure them to determine what they can not achieve in certain cases due to either anomalies or permanent failures during task execution. Robots need to identify these situations, and reveal the reasons behind these cases to overcome and recover from them. In this paper, we propose and analyze a Long Short-Term Memories-based (LSTM-based) awareness approach to reveal the reasons behind an anomaly case that occurs during a manipulation episode in an unstructured environment. The proposed method takes into account the real-time observations of the robot by fusing visual, auditory and proprioceptive sensory modalities to achieve this task. We also provide a comparative analysis of our method with Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs). The symptoms of anomalies are first learned from a given training set, then they can be classified in real-time based on the learned models. The approaches are evaluated on a Baxter robot executing object manipulation scenarios. The results indicate that the LSTM-based method outperforms the other methods with a 0.94 classification rate in revealing causes of anomalies in case of an unexpected deviation.

Includes members:
  Member "a multimodal anomaly detector for robot assisted feeding using an lstm based variational autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection")
  Member "fast robust and versatile event detection through hmm belief state gradient measures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "endowing robots with longer term autonomy by recovering from external disturbances in manipulation through grounded anomaly classification and recovery policies")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search 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.RO(Robotics)