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: "cassl curriculum accelerated self supervised learning"
  Abstract: Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framework for high-dimensional control require either scaling up the data collection efforts or using a clever sampling strategy for training. We present a novel approach - Curriculum Accelerated Self-Supervised Learning (CASSL) - to train policies that map visual information to high-level, higher- dimensional action spaces. CASSL orders the sampling of training data based on control dimensions: the learning and sampling are focused on few control parameters before other parameters. The right curriculum for learning is suggested by variance-based global sensitivity analysis of the control space. We apply our CASSL framework to learning how to grasp using an adaptive, underactuated multi-fingered gripper, a challenging system to control. Our experimental results indicate that CASSL provides significant improvement and generalization compared to baseline methods such as staged curriculum learning (8% increase) and complete end-to-end learning with random exploration (14% improvement) tested on a set of novel objects.

Connected through hyperedge HE-1 (induced by source paper "curriculum audiovisual learning"):
  with members:
    - "recursive visual sound separation using minus plus net"
    - "the sound of motions"
    - "co separating sounds of visual objects"
    - "learning to localize sound source in visual scenes"
    - "deep multimodal clustering for unsupervised audiovisual learning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "learning to separate object sounds by watching unlabeled video"
    - "the sound of pixels"

Connected through hyperedge HE-2 (induced by source paper "self supervised visual feature learning with curriculum"):
  with members:
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "advancements in image classification using convolutional neural network"
    - "unsupervised representation learning by predicting image rotations"
    - "automated curriculum learning for neural networks"
    - "learning representations for automatic colorization"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "unsupervised visual representation learning by context prediction"

Connected through hyperedge HE-3 (induced by source paper "towards a hypothesis on visual transformation based self supervision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "self supervised dialogue learning"
    - "contrastive multiview coding"
    - "self supervised exploration via disagreement"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "the visual task adaptation benchmark"
    - "self supervised visual feature learning with deep neural networks a survey"

Connected through hyperedge HE-4 (induced by source paper "learning to manipulate deformable objects without demonstrations"):
  with members:
    - "deep imitation learning of sequential fabric smoothing policies"
    - "learning robotic manipulation through visual planning and acting"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "robot bed making deep transfer learning using depth sensing of deformable fabric"
    - "deepmind control suite"
    - "model ensemble trust region policy optimization"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-5 (induced by source paper "growing action spaces"):
  with members:
    - "the starcraft multi agent challenge"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "modular architecture for starcraft ii with deep reinforcement learning"
    - "mix match agent curricula for reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "automated curriculum learning for neural networks"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "6 dof grasping for target driven object manipulation in clutter"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "robot learning in homes improving generalization and reducing dataset bias"

Connected through hyperedge HE-7 (induced by source paper "multiple interactions made easy mime large scale demonstrations data for imitation"):
  with members:
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "one shot visual imitation learning via meta learning"
    - "non local neural networks"
    - "carla an open urban driving simulator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "matterport3d learning from rgb d data in indoor environments"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"

Connected through hyperedge HE-8 (induced by source paper "learning to grasp without seeing"):
  with members:
    - "semi supervised haptic material recognition for robots using generative adversarial networks"
    - "learning to represent haptic feedback for partially observable tasks"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "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"

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)