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: "probabilistically safe robot planning with confidence based human predictions"
  Abstract: In order to safely operate around humans, robots can employ predictive models of human motion. Unfortunately, these models cannot capture the full complexity of human behavior and necessarily introduce simplifying assumptions. As a result, predictions may degrade whenever the observed human behavior departs from the assumed structure, which can have negative implications for safety. In this paper, we observe that how "rational" human actions appear under a particular model can be viewed as an indicator of that model's ability to describe the human's current motion. By reasoning about this model confidence in a real-time Bayesian framework, we show that the robot can very quickly modulate its predictions to become more uncertain when the model performs poorly. Building on recent work in provably-safe trajectory planning, we leverage these confidence-aware human motion predictions to generate assured autonomous robot motion. Our new analysis combines worst-case tracking error guarantees for the physical robot with probabilistic time-varying human predictions, yielding a quantitative, probabilistic safety certificate. We demonstrate our approach with a quadcopter navigating around a human.

Connected through hyperedge HE-1 (induced by source paper "quantifying hypothesis space misspecification in learning from human robot demonstrations and physical corrections"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "autonomous interpretation of demonstrations for modification of dynamical movement primitives"
    - "risk aware active inverse reinforcement learning"
    - "an algorithmic perspective on imitation learning"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "including uncertainty when learning from human corrections"
    - "learning under misspecified objective spaces"
    - "trajectory deformations from physical human robot interaction"

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"
    - "shared autonomy via deep reinforcement learning"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-3 (induced by source paper "discrete residual flow for probabilistic pedestrian behavior prediction"):
  with members:
    - "human motion trajectory prediction a survey"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "naturalistic driver intention and path prediction using recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "human robot collaboration via deep reinforcement learning of real world interactions"):
  with members:
    - "openspiel a framework for reinforcement learning in games"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shared autonomy via deep reinforcement learning"
    - "learning to walk via deep reinforcement learning"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "socially aware motion planning with deep reinforcement learning"
    - "sim to real robot learning from pixels with progressive nets"

Connected through hyperedge HE-5 (induced by source paper "online monitoring for safe pedestrian vehicle interactions"):
  with members:
    - "numerical verification of affine systems with up to a billion dimensions"
    - "yolov3 an incremental improvement"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "evaluation of automated vehicles encountering pedestrians at unsignalized crossings"
    - "dryvr data driven verification and compositional reasoning for automotive systems"

Connected through hyperedge HE-6 (induced by source paper "efficient probabilistic collision detection for non gaussian noise distributions"):
  with members:
    - "non gaussian slap simultaneous localization and planning under non gaussian uncertainty in static and dynamic environments"
    - "fast and bounded probabilistic collision detection in dynamic environments for high dof trajectory planning"
    - "efficient probabilistic collision detection for non convex shapes"
    - "i planner intention aware motion planning using learning based human motion prediction"

Connected through hyperedge HE-7 (induced by source paper "a scalable framework for real time multi robot multi human collision avoidance"):
  with members:
    - "classification based approximate reachability with guarantees applied to safe trajectory tracking"
    - "a classification based approach for approximate reachability"
    - "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
    - "robust tracking with model mismatch for fast and safe planning an sos optimization approach"
    - "cooperative multi agent planning a survey"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-8 (induced by source paper "hierarchical game theoretic planning for autonomous vehicles"):
  with members:
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "multimodal probabilistic model based planning for human robot interaction"
    - "guided cost learning deep inverse optimal control via policy optimization"

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.RO(Robotics)