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: "doomed direct online optimization of modeling errors in dynamics"
  Abstract: It has long been hoped that model-based control will improve tracking performance while maintaining or increasing compliance. This hope hinges on having or being able to estimate an accurate inverse dynamics model. As a result, substantial effort has gone into modeling and estimating dynamics (error) models. Most recent research has focused on learning the true inverse dynamics using data points mapping observed accelerations to the torques used to generate them. Unfortunately, if the initial tracking error is bad, such learning processes may train substantially off-distribution to predict well on actual observed acceleration rather then the desired accelerations. This work takes a different approach. We define a class of gradient-based online learning algorithms we term Direct Online Optimization for Modeling Errors in Dynamics (DOOMED) that directly minimize an objective measuring the divergence between actual and desired accelerations. Our objective is defined in terms of the true system's unknown dynamics and is therefore impossible to evaluate. However, we show that its gradient is measurable online from system data. We develop a novel adaptive control approach based on running online learning to directly correct (inverse) dynamics errors in real time using the data stream from the robot to accurately achieve desired accelerations during execution.

Connected through hyperedge HE-1 (induced by source paper "learning state dependent losses for inverse dynamics learning"):
  with members:
    - "encoding physical constraints in differentiable newton euler algorithm"
    - "meta learning via learned loss"
    - "generalized inner loop meta learning"
    - "pytorch an imperative style high performance deep learning library"
    - "evolved policy gradients"
    - "deep online learning via meta learning continual adaptation for model based rl"
    - "online learning of a memory for learning rates"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-2 (induced by source paper "a general framework for structured learning of mechanical systems"):
  with members:
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "decoupling dynamics and reward for transfer learning"
    - "backprop kf learning discriminative deterministic state estimators"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "deep lagrangian networks for end to end learning of energy based control for under actuated systems"):
  with members:
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "graph networks as learnable physics engines for inference and control"
    - "proximal policy optimization algorithms"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "real time perception meets reactive motion generation"):
  with members:
    - "riemannian motion policies"
    - "learning feedback terms for reactive planning and control"
    - "probabilistic articulated real time tracking for robot manipulation"
    - "idrm humanoid motion planning with real time end pose selection in complex environments"
    - "analysis and observations from the first amazon picking challenge"
    - "end to end training of deep visuomotor policies"
    - "probabilistic object tracking using a range camera"

Connected through hyperedge HE-5 (induced by source paper "a new data source for inverse dynamics learning"):
  with members:
    - "transfer from simulation to real world through learning deep inverse dynamics model"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)