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 "deep multimodal embedding manipulating novel objects with point clouds language and trajectories"):
  Abstract: A robot operating in a real-world environment needs to perform reasoning over a variety of sensor modalities such as vision, language and motion trajectories. However, it is extremely challenging to manually design features relating such disparate modalities. In this work, we introduce an algorithm that learns to embed point-cloud, natural language, and manipulation trajectory data into a shared embedding space with a deep neural network. To learn semantically meaningful spaces throughout our network, we use a loss-based margin to bring embeddings of relevant pairs closer together while driving less-relevant cases from different modalities further apart. We use this both to pre-train its lower layers and fine-tune our final embedding space, leading to a more robust representation. We test our algorithm on the task of manipulating novel objects and appliances based on prior experience with other objects. On a large dataset, we achieve significant improvements in both accuracy and inference time over the previous state of the art. We also perform end-to-end experiments on a PR2 robot utilizing our learned embedding space.

Includes members:
  Member "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sub goal trees a framework for goal based reinforcement learning")
  Member "learning contact rich manipulation skills with guided policy search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "let s push things forward a survey on robot pushing")
  Member "synthesizing manipulation sequences for under specified tasks using unrolled markov random fields":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural task planning with and or graph representations")
  Member "deep learning for detecting robotic grasps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "exploiting similarities among languages for machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "adadelta an adaptive learning rate method":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "theano new features and speed improvements":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mnn a universal and efficient inference engine")
  Member "difference of normals as a multi scale operator in unorganized point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "monocular trail detection and tracking aided by visual slam for small unmanned aerial vehicles")

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)