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: "semi supervised haptic material recognition for robots using generative adversarial networks"
  Abstract: Material recognition enables robots to incorporate knowledge of material properties into their interactions with everyday objects. For example, material recognition opens up opportunities for clearer communication with a robot, such as "bring me the metal coffee mug", and recognizing plastic versus metal is crucial when using a microwave or oven. However, collecting labeled training data with a robot is often more difficult than unlabeled data. We present a semi-supervised learning approach for material recognition that uses generative adversarial networks (GANs) with haptic features such as force, temperature, and vibration. Our approach achieves state-of-the-art results and enables a robot to estimate the material class of household objects with ~90% accuracy when 92% of the training data are unlabeled. We explore how well this approach can recognize the material of new objects and we discuss challenges facing generalization. To motivate learning from unlabeled training data, we also compare results against several common supervised learning classifiers. In addition, we have released the dataset used for this work which consists of time-series haptic measurements from a robot that conducted thousands of interactions with 72 household objects.

Connected through hyperedge HE-1 (induced by source paper "behavioural repertoire via generative adversarial policy networks"):
  with members:
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "diversity is all you need learning skills without a reward function"
    - "multi task domain adaptation for deep learning of instance grasping from simulation"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration"
    - "improved training of wasserstein gans"
    - "quality and diversity optimization a unifying modular framework"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-2 (induced by source paper "robot learning in homes improving generalization and reducing dataset bias"):
  with members:
    - "learning to grasp without seeing"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "asymmetric actor critic for image based robot learning"
    - "unsupervised learning for physical interaction through video prediction"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "learning to push by grasping using multiple tasks for effective learning"

Connected through hyperedge HE-3 (induced by source paper "learning to grasp without seeing"):
  with members:
    - "learning to represent haptic feedback for partially observable tasks"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "cassl curriculum accelerated self supervised learning"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "analysis and observations from the first amazon picking challenge"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "embed to control a locally linear latent dynamics model for control from raw images"

Connected through hyperedge HE-4 (induced by source paper "classification of household materials via spectroscopy"):
  with members:
    - "aggregated residual transformations for deep neural networks"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "deep residual learning for image recognition"
    - "deep learning for tactile understanding from visual and haptic data"
    - "material recognition in the wild with the materials in context database"

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)