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: "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.

Connected through hyperedge HE-1 (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"
    - "deep modular co attention networks for visual question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "interactive visual grounding of referring expressions for human robot interaction"

Connected through hyperedge HE-2 (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"
    - "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"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"

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)