human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "urban2vec incorporating street view imagery and pois for multi modal urban neighborhood embedding"):
  with members:
    - "learning from discovering an unsupervised approach to geographical knowledge discovery using street level and street network images"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "predicting twitter user socioeconomic attributes with network and language information"
    - "node2vec scalable feature learning for networks"
    - "learning deep structure preserving image text embeddings"
    - "rethinking the inception architecture for computer vision"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning"):
  with members:
    - "fusion volumetric object level slam"
    - "interactive perception leveraging action in perception and perception in action"
    - "exploiting symmetry and or manhattan properties for 3d object structure estimation from single and multiple images"
    - "shape completion enabled robotic grasping"
    - "analysis and observations from the first amazon picking challenge"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "a dataset for improved rgbd based object detection and pose estimation for warehouse pick and place"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"

Connected through hyperedge HE-3 (induced by source paper "reasoning on grasp action affordances"):
  with members:
    - "learning and composing primitive skills for dual arm manipulation"
    - "learning and generalisation of primitives skills towards robust dual arm manipulation"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "deep residual learning for image recognition"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-4 (induced by source paper "task embedded control networks for few shot imitation learning"):
  with members:
    - "sim to real reinforcement learning for deformable object manipulation"
    - "one shot visual imitation learning via meta learning"
    - "prototypical networks for few shot learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "few shot learning through an information retrieval lens"

Connected through hyperedge HE-5 (induced by source paper "modeling grasp motor imagery through deep conditional generative models"):
  with members:
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "deep spatial autoencoders for visuomotor learning"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "end to end training of deep visuomotor policies"
    - "deep learning for detecting robotic grasps"
    - "network in network"

Question: Please tell me which class the center node 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)