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: "ddco discovery of deep continuous options for robot learning from demonstrations"
  Abstract: An option is a short-term skill consisting of a control policy for a specified region of the state space, and a termination condition recognizing leaving that region. In prior work, we proposed an algorithm called Deep Discovery of Options (DDO) to discover options to accelerate reinforcement learning in Atari games. This paper studies an extension to robot imitation learning, called Discovery of Deep Continuous Options (DDCO), where low-level continuous control skills parametrized by deep neural networks are learned from demonstrations. We extend DDO with: (1) a hybrid categorical-continuous distribution model to parametrize high-level policies that can invoke discrete options as well continuous control actions, and (2) a cross-validation method that relaxes DDO's requirement that users specify the number of options to be discovered. We evaluate DDCO in simulation of a 3-link robot in the vertical plane pushing a block with friction and gravity, and in two physical experiments on the da Vinci surgical robot, needle insertion where a needle is grasped and inserted into a silicone tissue phantom, and needle bin picking where needles and pins are grasped from a pile and categorized into bins. In the 3-link arm simulation, results suggest that DDCO can take 3x fewer demonstrations to achieve the same reward compared to a baseline imitation learning approach. In the needle insertion task, DDCO was successful 8/10 times compared to the next most accurate imitation learning baseline 6/10. In the surgical bin picking task, the learned policy successfully grasps a single object in 66 out of 99 attempted grasps, and in all but one case successfully recovered from failed grasps by retrying a second time.

Connected through hyperedge HE-1 (induced by source paper "applying depth sensing to automated surgical manipulation with a da vinci robot"):
  with members:
    - "autonomous tissue manipulation via surgical robot using learning based model predictive control"
    - "deep imitation learning of sequential fabric smoothing policies"
    - "safety augmented value estimation from demonstrations saved safe deep model based rl for sparse cost robotic tasks"
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "open sourced reinforcement learning environments for surgical robotics"
    - "toward synergic learning for autonomous manipulation of deformable tissues via surgical robots an approximate q learning approach"
    - "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"

Connected through hyperedge HE-2 (induced by source paper "exploiting hierarchy for learning and transfer in kl regularized rl"):
  with members:
    - "information asymmetry in kl regularized rl"
    - "learning to share and hide intentions using information regularization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-3 (induced by source paper "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"):
  with members:
    - "learning robotic manipulation through visual planning and acting"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning latent plans from play"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "robot motion planning in learned latent spaces"
    - "hierarchical reinforcement learning with hindsight"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-4 (induced by source paper "conditional driving from natural language instructions"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "prospection interpretable plans from language by predicting the future"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "driving policy transfer via modularity and abstraction"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"

Connected through hyperedge HE-5 (induced by source paper "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"):
  with members:
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "large scale study of curiosity driven learning"
    - "distributed prioritized experience replay"
    - "episodic curiosity through reachability"
    - "eigenoption discovery through the deep successor representation"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "plots procedure learning from observations using subtask structure"):
  with members:
    - "composable planning with attributes"
    - "reinforcement learning on web interfaces using workflow guided exploration"
    - "one shot learning of multi step tasks from observation via activity localization in auxiliary video"
    - "taco learning task decomposition via temporal alignment for control"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "behavioral cloning from observation"
    - "one shot visual imitation learning via meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"

Connected through hyperedge HE-7 (induced by source paper "the termination critic"):
  with members:
    - "diversity is all you need learning skills without a reward function"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "the option critic architecture"
    - "variational intrinsic control"

Connected through hyperedge HE-8 (induced by source paper "few shot bayesian imitation learning with logical program policies"):
  with members:
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "deep q learning from demonstrations"
    - "learning to infer graphics programs from hand drawn images"
    - "fully convolutional networks for semantic segmentation"

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)