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: "shared autonomy via deep reinforcement learning"
  Abstract: In shared autonomy, user input is combined with semi-autonomous control to achieve a common goal. The goal is often unknown ex-ante, so prior work enables agents to infer the goal from user input and assist with the task. Such methods tend to assume some combination of knowledge of the dynamics of the environment, the user's policy given their goal, and the set of possible goals the user might target, which limits their application to real-world scenarios. We propose a deep reinforcement learning framework for model-free shared autonomy that lifts these assumptions. We use human-in-the-loop reinforcement learning with neural network function approximation to learn an end-to-end mapping from environmental observation and user input to agent action, with task reward as the only form of supervision. Controlled studies with users (n = 16) and synthetic pilots playing a video game and flying a real quadrotor demonstrate the ability of our algorithm to assist users with real-time control tasks in which the agent cannot directly access the user's private information through observations, but receives a reward signal and user input that both depend on the user's intent. The agent learns to assist the user without access to this private information, implicitly inferring it from the user's input. This allows the assisted user to complete the task more effectively than the user or an autonomous agent could on their own. This paper is a proof of concept that illustrates the potential for deep reinforcement learning to enable flexible and practical assistive systems.

Connected through hyperedge HE-1 (induced by source paper "real world human robot collaborative reinforcement learning"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "human visual attention prediction boosts learning performance of autonomous driving agents"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "pytorch an imperative style high performance deep learning library"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "machine theory of mind"
    - "learning to walk via deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "carpal confidence aware intent recognition for parallel autonomy"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "the trajectron probabilistic multi agent trajectory modeling with dynamic spatiotemporal graphs"
    - "safe end to end imitation learning for model predictive control"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-3 (induced by source paper "a general arbitration model for robust human robot shared control with multi source uncertainty modeling"):
  with members:
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "human robot mutual adaptation in shared autonomy"
    - "uncertainty based arbitration of human machine shared control"
    - "autonomy infused teleoperation with application to bci manipulation"
    - "shared autonomy via hindsight optimization"

Connected through hyperedge HE-4 (induced by source paper "learn task first or learn human partner first deep reinforcement learning of human robot cooperation in asymmetric hierarchical dynamic task"):
  with members:
    - "human robot mutual adaptation in shared autonomy"
    - "shared autonomy via hindsight optimization"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "learning human objectives by evaluating hypothetical behavior"):
  with members:
    - "improving safety in reinforcement learning using model based architectures and human intervention"
    - "model based reinforcement learning for atari"
    - "risk aware active inverse reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "safe exploration in continuous action spaces"
    - "supervising strong learners by amplifying weak experts"
    - "scalable agent alignment via reward modeling a research direction"
    - "active inverse reward design"

Connected through hyperedge HE-6 (induced by source paper "robot learning via human adversarial games"):
  with members:
    - "deep reinforcement learning from policy dependent human feedback"
    - "explore exploit or listen combining human feedback and policy model to speed up deep reinforcement learning in 3d worlds"
    - "interactive learning from policy dependent human feedback"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "learning to push by grasping using multiple tasks for effective learning"
    - "supervision via competition robot adversaries for learning tasks"
    - "learning to poke by poking experiential learning of intuitive physics"

Connected through hyperedge HE-7 (induced by source paper "on the utility of model learning in hri"):
  with members:
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "proximal policy optimization algorithms"
    - "multimodal probabilistic model based planning for human robot interaction"
    - "unsupervised learning for physical interaction through video prediction"
    - "trust region policy optimization"
    - "shared autonomy via hindsight optimization"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "an extensible interactive interface for agent design"):
  with members:
    - "variational inverse control with events a general framework for data driven reward definition"
    - "visual reinforcement learning with imagined goals"
    - "reward learning from human preferences and demonstrations in atari"
    - "learning to understand goal specifications by modelling reward"
    - "guiding policies with language via meta learning"
    - "hindsight experience replay"
    - "grounded language learning in a simulated 3d world"
    - "proximal policy optimization algorithms"

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)