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 "grounding language attributes to objects using bayesian eigenobjects"):
  Abstract: We develop a system to disambiguate objects based on simple physical descriptions. The system takes as input a natural language phrase and a depth image containing a segmented object and predicts how similar the observed object is to the described object. Our system is designed to learn from only a small amount of human-labeled language data and generalize to viewpoints not represented in the language-annotated depth-image training set. By decoupling 3D shape representation from language representation, our method is able to ground language to novel objects using a small amount of language-annotated depth-data and a larger corpus of unlabeled 3D object meshes, even when these objects are partially observed from unusual viewpoints. Our system is able to disambiguate between novel objects, observed via depth-images, based on natural language descriptions. Our method also enables view-point transfer; trained on human-annotated data on a small set of depth-images captured from frontal viewpoints, our system successfully predicted object attributes from rear views despite having no such depth images in its training set. Finally, we demonstrate our system on a Baxter robot, enabling it to pick specific objects based on human-provided natural language descriptions.

Includes members:
  Member "interactive visual grounding of referring expressions for human robot interaction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations")
  Member "hybrid bayesian eigenobjects combining linear subspace and deep network methods for 3d robot vision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning vs traditional computer vision")
  Member "text2shape generating shapes from natural language by learning joint embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "interactively picking real world objects with unconstrained spoken language instructions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations")
  Member "shapenet an information rich 3d model repository":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "towards universal paraphrastic sentence embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving candidate generation for low resource cross lingual entity linking")
  Member "natural language object retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation")
  Member "3d shapenets a deep representation for volumetric shapes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

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)