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: "interactively picking real world objects with unconstrained spoken language instructions"
  Abstract: Comprehension of spoken natural language is an essential component for robots to communicate with human effectively. However, handling unconstrained spoken instructions is challenging due to (1) complex structures including a wide variety of expressions used in spoken language and (2) inherent ambiguity in interpretation of human instructions. In this paper, we propose the first comprehensive system that can handle unconstrained spoken language and is able to effectively resolve ambiguity in spoken instructions. Specifically, we integrate deep-learning-based object detection together with natural language processing technologies to handle unconstrained spoken instructions, and propose a method for robots to resolve instruction ambiguity through dialogue. Through our experiments on both a simulated environment as well as a physical industrial robot arm, we demonstrate the ability of our system to understand natural instructions from human operators effectively, and how higher success rates of the object picking task can be achieved through an interactive clarification process.

Connected through hyperedge HE-1 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations"):
  with members:
    - "self supervised 3d shape and viewpoint estimation from single images for robotics"
    - "fast scnn fast semantic segmentation network"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "referring relationships"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "context aware synthesis and placement of object instances"
    - "mattnet modular attention network for referring expression comprehension"
    - "a multimodal classifier generative adversarial network for carry and place tasks from ambiguous language instructions"

Connected through hyperedge HE-2 (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "combined task and action learning from human demonstrations for mobile manipulation applications"
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "robot learning and execution of collaborative manipulation plans from youtube videos"
    - "integrating knowledge and reasoning in image understanding"
    - "improving grounded natural language understanding through human robot dialog"
    - "learning actions from human demonstration video for robotic manipulation"
    - "prospection interpretable plans from language by predicting the future"

Connected through hyperedge HE-3 (induced by source paper "vision based robot manipulation learning via human demonstrations"):
  with members:
    - "robot learning and execution of collaborative manipulation plans from youtube videos"
    - "learning actions from human demonstration video for robotic manipulation"
    - "an algorithmic perspective on imitation learning"
    - "one shot observation learning using visual activity features"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "one shot visual imitation learning via meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "intel realsense stereoscopic depth cameras"

Connected through hyperedge HE-4 (induced by source paper "invisible marker automatic annotation of segmentation masks for object manipulation"):
  with members:
    - "affordance learning for end to end visuomotor robot control"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "recent trends in deep learning based natural language processing"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "detection and tracking of liquids with fully convolutional networks"
    - "coco stuff thing and stuff classes in context"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "autonomous planning based on spatial concepts to tidy up home environments with service robots"):
  with members:
    - "yolov3 an incremental improvement"
    - "multimodal sensor based semantic 3d mapping for a large scale environment"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences"
    - "ssd single shot multibox detector"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "a multimodal target source classifier with attention branches to understand ambiguous instructions for fetching daily objects"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal unified attention networks for vision and language interactions"
    - "grounding language attributes to objects using bayesian eigenobjects"
    - "multimodal attention branch network for perspective free sentence generation"
    - "tvqa spatio temporal grounding for video question answering"
    - "understanding natural language instructions for fetching daily objects using gan based multimodal target source classification"
    - "deep modular co attention networks for visual question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-7 (induced by source paper "constructing dynamic knowledge graph for visual semantic understanding and applications in autonomous robotics"):
  with members:
    - "visual query answering by entity attribute graph matching and reasoning"
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "on exploring undetermined relationships for visual relationship detection"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "pointing novel objects in image captioning"
    - "improving grounded natural language understanding through human robot dialog"
    - "context and attribute grounded dense captioning"
    - "connecting touch and vision via cross modal prediction"

Connected through hyperedge HE-8 (induced by source paper "multimodal attention branch network for perspective free sentence generation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "grounding language attributes to objects using bayesian eigenobjects"
    - "learning to generate unambiguous spatial referring expressions for real world environments"
    - "understanding natural language instructions for fetching daily objects using gan based multimodal target source classification"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "sim to real learning agile locomotion for quadruped robots"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "a multimodal classifier generative adversarial network for carry and place tasks from ambiguous language instructions"

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)