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 "learning to generate unambiguous spatial referring expressions for real world environments"):
  Abstract: Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We evaluate our method in ambiguous environments (e.g., environments that include very similar objects with similar relationships) relative to a state-of-the-art algorithm. We show that our method generates referring expressions that people find to be more accurate ($\sim$30% better) and would prefer to use ($\sim$32% more often).

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 "using syntax to ground referring expressions in natural images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "referring expression grounding by marginalizing scene graph likelihood")
  Member "interactively picking real world objects with unconstrained spoken language instructions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations")
  Member "identifying spatial relations in images using convolutional neural networks":
    also connected through other hyperedges:
      (none)
  Member "grounding spatio semantic referring expressions for human robot interaction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "referring relationships")
  Member "a joint speaker listener reinforcer model for referring expressions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "speed accuracy trade offs for modern convolutional object detectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation")
  Member "generation and comprehension of unambiguous object descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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)