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: "setting up a reinforcement learning task with a real world robot"
  Abstract: Reinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks. However, learning with real-world robots is often unreliable and difficult, which resulted in their low adoption in reinforcement learning research. This difficulty is worsened by the lack of guidelines for setting up learning tasks with robots. In this work, we develop a learning task with a UR5 robotic arm to bring to light some key elements of a task setup and study their contributions to the challenges with robots. We find that learning performance can be highly sensitive to the setup, and thus oversights and omissions in setup details can make effective learning, reproducibility, and fair comparison hard. Our study suggests some mitigating steps to help future experimenters avoid difficulties and pitfalls. We show that highly reliable and repeatable experiments can be performed in our setup, indicating the possibility of reinforcement learning research extensively based on real-world robots.

Connected through hyperedge HE-1 (induced by source paper "composing diverse policies for temporally extended tasks"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "deepmdp learning continuous latent space models for representation learning"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "learning safe policies with expert guidance"

Connected through hyperedge HE-2 (induced by source paper "dynamical distance learning for semi supervised and unsupervised skill discovery"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "how much do unstated problem constraints limit deep robotic reinforcement learning"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "deep reinforcement learning that matters"
    - "hindsight experience replay"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "towards generalization and simplicity in continuous control"
    - "reinforcement learning with deep energy based policies"

Connected through hyperedge HE-4 (induced by source paper "autoregressive policies for continuous control deep reinforcement learning"):
  with members:
    - "making deep q learning methods robust to time discretization"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "large scale study of curiosity driven learning"
    - "soft actor critic algorithms and applications"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "parameter space noise for exploration"

Connected through hyperedge HE-5 (induced by source paper "a survey on reproducibility by evaluating deep reinforcement learning algorithms on real world robots"):
  with members:
    - "a study on overfitting in deep reinforcement learning"
    - "terminologies for reproducible research"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "deep reinforcement learning that matters"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization algorithms"
    - "benchmarking deep reinforcement learning for continuous control"

Connected through hyperedge HE-6 (induced by source paper "benchmarking reinforcement learning algorithms on real world robots"):
  with members:
    - "deepmind control suite"
    - "reactive reinforcement learning in asynchronous environments"
    - "deep reinforcement learning that matters"
    - "mutual alignment transfer learning"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "learnings options end to end for continuous action tasks"
    - "deep q learning from demonstrations"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-7 (induced by source paper "a practical approach to insertion with variable socket position using deep reinforcement learning"):
  with members:
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "a framework for robot manipulation skill formalism meta learning and adaptive control"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "deep reinforcement learning for high precision assembly tasks"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "a distributional perspective on reinforcement learning"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "pves position velocity encoders for unsupervised learning of structured state representations"

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)