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: "reinforcement learning with a corrupted reward channel"
  Abstract: No real-world reward function is perfect. Sensory errors and software bugs may result in RL agents observing higher (or lower) rewards than they should. For example, a reinforcement learning agent may prefer states where a sensory error gives it the maximum reward, but where the true reward is actually small. We formalise this problem as a generalised Markov Decision Problem called Corrupt Reward MDP. Traditional RL methods fare poorly in CRMDPs, even under strong simplifying assumptions and when trying to compensate for the possibly corrupt rewards. Two ways around the problem are investigated. First, by giving the agent richer data, such as in inverse reinforcement learning and semi-supervised reinforcement learning, reward corruption stemming from systematic sensory errors may sometimes be completely managed. Second, by using randomisation to blunt the agent's optimisation, reward corruption can be partially managed under some assumptions.

Connected through hyperedge HE-1 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty"):
  with members:
    - "contextual imagined goals for self supervised robotic learning"
    - "reward shaping via meta learning"
    - "dynamical distance learning for unsupervised and semi supervised skill discovery"
    - "motion perception in reinforcement learning with dynamic objects"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning with perturbed rewards"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-2 (induced by source paper "manipulating reinforcement learning poisoning attacks on cost signals"):
  with members:
    - "deceptive reinforcement learning under adversarial manipulations on cost signals"
    - "policy poisoning in batch reinforcement learning and control"
    - "adaptive honeypot engagement through reinforcement learning of semi markov decision processes"
    - "reinforcement learning with perturbed rewards"
    - "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"
    - "the faults in our pi stars security issues and open challenges in deep reinforcement learning"
    - "tactics of adversarial attack on deep reinforcement learning agents"

Connected through hyperedge HE-3 (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-4 (induced by source paper "doubly robust off policy actor critic algorithms for reinforcement learning"):
  with members:
    - "when to trust your model model based policy optimization"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "importance sampling policy evaluation with an estimated behavior policy"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "more robust doubly robust off policy evaluation"
    - "reward estimation for variance reduction in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"

Connected through hyperedge HE-5 (induced by source paper "deceptive reinforcement learning under adversarial manipulations on cost signals"):
  with members:
    - "game theoretic analysis of cyber deception evidence based strategies and dynamic risk mitigation"
    - "reinforcement learning with perturbed rewards"
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"
    - "istrict an interdependent strategic trust mechanism for the cloud enabled internet of controlled things"
    - "the faults in our pi stars security issues and open challenges in deep reinforcement learning"
    - "optimal timing in dynamic and robust attacker engagement during advanced persistent threats"
    - "a game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"

Connected through hyperedge HE-6 (induced by source paper "embedded agency"):
  with members:
    - "supervising strong learners by amplifying weak experts"
    - "categorizing variants of goodhart s law"
    - "occam s razor is insufficient to infer the preferences of irrational agents"
    - "functional decision theory a new theory of instrumental rationality"
    - "cooperative inverse reinforcement learning"
    - "nonparametric general reinforcement learning"
    - "concrete problems in ai safety"
    - "a formal solution to the grain of truth problem"

Connected through hyperedge HE-7 (induced by source paper "advantage amplification in slowly evolving latent state environments"):
  with members:
    - "reinforcement learning based recommender system using biclustering technique"
    - "deep reinforcement learning for list wise recommendations"
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "taming the noise in reinforcement learning via soft updates"
    - "trust region policy optimization"
    - "tractable inference for complex stochastic processes"
    - "hierarchical solution of markov decision processes using macro actions"

Connected through hyperedge HE-8 (induced by source paper "a legal definition of ai"):
  with members:
    - "identifying and correcting label bias in machine learning"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "learning dexterous in hand manipulation"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "accountability of ai under the law the role of explanation"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "deep speech 2 end to end speech recognition in english and mandarin"

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.AI(Artificial Intelligence)