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: "end to end robotic reinforcement learning without reward engineering"
  Abstract: The combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory inputs, such as camera images, effectively subsuming both estimation and control into one model. However, real-world applications of reinforcement learning must specify the goal of the task by means of a manually programmed reward function, which in practice requires either designing the very same perception pipeline that end-to-end reinforcement learning promises to avoid, or else instrumenting the environment with additional sensors to determine if the task has been performed successfully. In this paper, we propose an approach for removing the need for manual engineering of reward specifications by enabling a robot to learn from a modest number of examples of successful outcomes, followed by actively solicited queries, where the robot shows the user a state and asks for a label to determine whether that state represents successful completion of the task. While requesting labels for every single state would amount to asking the user to manually provide the reward signal, our method requires labels for only a tiny fraction of the states seen during training, making it an efficient and practical approach for learning skills without manually engineered rewards. We evaluate our method on real-world robotic manipulation tasks where the observations consist of images viewed by the robot's camera. In our experiments, our method effectively learns to arrange objects, place books, and drape cloth, directly from images and without any manually specified reward functions, and with only 1-4 hours of interaction with the real world.

Connected through hyperedge HE-1 (induced by source paper "a framework for data driven robotics"):
  with members:
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "challenges of real world reinforcement learning"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-2 (induced by source paper "task relevant adversarial imitation learning"):
  with members:
    - "observe and look further achieving consistent performance on atari"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "spectral normalization for generative adversarial networks"
    - "sample efficient imitation learning via generative adversarial nets"
    - "distributed prioritized experience replay"
    - "large scale gan training for high fidelity natural image synthesis"
    - "distributed distributional deterministic policy gradients"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"

Connected through hyperedge HE-3 (induced by source paper "dynamical distance learning for semi supervised and unsupervised skill discovery"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "robel robotics benchmarks for learning with low cost robots"
    - "self supervised learning of image embedding for continuous control"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "temporal difference models model free deep rl for model based control"
    - "composable deep reinforcement learning for robotic manipulation"
    - "visual reinforcement learning with imagined goals"
    - "semi parametric topological memory for navigation"

Connected through hyperedge HE-4 (induced by source paper "accept synthetic objects as real end to end training of attentive deep visuomotor policies for manipulation in clutter"):
  with members:
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "craves controlling robotic arm with a vision based economic system"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "deep object centric representations for generalizable robot learning"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "video fill in the blank using lr rl lstms with spatial temporal attentions"

Connected through hyperedge HE-5 (induced by source paper "if maxent rl is the answer what is the question"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "efficient exploration via state marginal matching"
    - "tsallis reinforcement learning a unified framework for maximum entropy reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "unsupervised control through non parametric discriminative rewards"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "variational bayesian reinforcement learning with regret bounds"

Connected through hyperedge HE-6 (induced by source paper "multi agent manipulation via locomotion using hierarchical sim2real"):
  with members:
    - "active domain randomization"
    - "hierarchical reinforcement learning for quadruped locomotion"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-7 (induced by source paper "interactive text ranking with bayesian optimisation a case study on community qa and summarisation"):
  with members:
    - "passage ranking with weak supervision"
    - "preference based interactive multi document summarisation"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "active learning for interactive neural machine translation of data streams"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "commonsense for generative multi hop question answering tasks"

Connected through hyperedge HE-8 (induced by source paper "learning deep parameterized skills from demonstration for re targetable visuomotor control"):
  with members:
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "goal conditioned imitation learning"
    - "a practical approach to insertion with variable socket position using deep reinforcement learning"
    - "pay attention robustifying a deep visuomotor policy through task focused attention"
    - "one shot visual imitation learning via meta learning"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "one shot imitation learning"
    - "model agnostic meta learning for fast adaptation of deep networks"

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.LG(Machine Learning)