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 "understanding natural language instructions for fetching daily objects using gan based multimodal target source classification"):
  Abstract: In this paper, we address multimodal language understanding for unconstrained fetching instruction in domestic service robots context. A typical fetching instruction such as "Bring me the yellow toy from the white shelf" requires to infer the user intention, that is what object (target) to fetch and from where (source). To solve the task, we propose a Multimodal Target-source Classifier Model (MTCM), which predicts the region-wise likelihood of target and source candidates in the scene. Unlike other methods, MTCM can handle regionwise classification based on linguistic and visual features. We evaluated our approach that outperformed the state-of-the-art method on a standard data set. In addition, we extended MTCM with Generative Adversarial Nets (MTCM-GAN), and enabled simultaneous data augmentation and classification.

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 "how good is my gan":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive density estimation for generative models")
  Member "a multimodal classifier generative adversarial network for carry and place tasks from ambiguous 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 "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "grounded language understanding for manipulation instructions using gan based classification":
    also connected through other hyperedges:
      (none)
  Member "using simulation and domain adaptation to improve efficiency of deep robotic grasping":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "interactively picking real world objects with unconstrained spoken language instructions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations")
  Member "photo realistic single image super resolution using a generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)