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: "leave no trace learning to reset for safe and autonomous reinforcement learning"
  Abstract: Deep reinforcement learning algorithms can learn complex behavioral skills, but real-world application of these methods requires a large amount of experience to be collected by the agent. In practical settings, such as robotics, this involves repeatedly attempting a task, resetting the environment between each attempt. However, not all tasks are easily or automatically reversible. In practice, this learning process requires extensive human intervention. In this work, we propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and reset policy, with the reset policy resetting the environment for a subsequent attempt. By learning a value function for the reset policy, we can automatically determine when the forward policy is about to enter a non-reversible state, providing for uncertainty-aware safety aborts. Our experiments illustrate that proper use of the reset policy can greatly reduce the number of manual resets required to learn a task, can reduce the number of unsafe actions that lead to non-reversible states, and can automatically induce a curriculum.

Connected through hyperedge HE-1 (induced by source paper "learning to walk in the real world with minimal human effort"):
  with members:
    - "safety guided deep reinforcement learning via online gaussian process estimation"
    - "solving rubik s cube with a robot hand"
    - "generalizing from a few environments in safety critical reinforcement learning"
    - "data efficient reinforcement learning for legged robots"
    - "deepgait planning and control of quadrupedal gaits using deep reinforcement learning"
    - "norml no reward meta learning"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-2 (induced by source paper "risk averse robust adversarial reinforcement learning"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "virtual to real reinforcement learning for autonomous driving"
    - "gradient free policy architecture search and adaptation"
    - "parameter space noise for exploration"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-3 (induced by source paper "evolutionary computation and ai safety research problems impeding routine and safe real world application of evolution"):
  with members:
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "agi safety literature review"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "supervising strong learners by amplifying weak experts"
    - "a study on overfitting in deep reinforcement learning"
    - "scalable agent alignment via reward modeling a research direction"
    - "quantifying generalization in reinforcement learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"

Connected through hyperedge HE-4 (induced by source paper "the regretful agent heuristic aided navigation through progress estimation"):
  with members:
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"
    - "gibson env real world perception for embodied agents"
    - "speaker follower models for vision and language navigation"
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-5 (induced by source paper "avid learning multi stage tasks via pixel level translation of human videos"):
  with members:
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "third person visual imitation learning via decoupled hierarchical controller"
    - "imitating latent policies from observation"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "learning latent dynamics for planning from pixels"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "state representation learning for control an overview"
    - "solar deep structured latent representations for model based reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "conservative agency via attainable utility preservation"):
  with members:
    - "preferences implicit in the state of the world"
    - "penalizing side effects using stepwise relative reachability"
    - "measuring and avoiding side effects using relative reachability"
    - "a lyapunov based approach to safe reinforcement learning"
    - "categorizing variants of goodhart s law"
    - "incomplete contracting and ai alignment"
    - "ai safety gridworlds"
    - "low impact artificial intelligences"

Connected through hyperedge HE-7 (induced by source paper "safety guided deep reinforcement learning via online gaussian process estimation"):
  with members:
    - "a lyapunov based approach to safe reinforcement learning"
    - "ai safety gridworlds"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "constrained policy optimization"
    - "on kernelized multi armed bandits"
    - "deep q learning from demonstrations"
    - "safe reinforcement learning via shielding"
    - "concrete problems in ai safety"

Connected through hyperedge HE-8 (induced by source paper "learning the arrow of time"):
  with members:
    - "time reversal as self supervision"
    - "measuring and avoiding side effects using relative reachability"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "episodic curiosity through reachability"
    - "recall traces backtracking models for efficient reinforcement learning"
    - "ai safety gridworlds"
    - "low impact artificial intelligences"

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)