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: "bayesian optimization using domain knowledge on the atrias biped"
  Abstract: Controllers in robotics often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. It is typical to use simulation to learn these parameters, but controllers learned in simulation often don't transfer to hardware. This necessitates optimization directly on hardware. However, collecting data on hardware can be expensive. This has led to a recent interest in adapting data-efficient learning techniques to robotics. One popular method is Bayesian Optimization (BO), a sample-efficient black-box optimization scheme, but its performance typically degrades in higher dimensions. We aim to overcome this problem by incorporating domain knowledge to reduce dimensionality in a meaningful way, with a focus on bipedal locomotion. In previous work, we proposed a transformation based on knowledge of human walking that projected a 16-dimensional controller to a 1-dimensional space. In simulation, this showed enhanced sample efficiency when optimizing human-inspired neuromuscular walking controllers on a humanoid model. In this paper, we present a generalized feature transform applicable to non-humanoid robot morphologies and evaluate it on the ATRIAS bipedal robot -- in simulation and on hardware. We present three different walking controllers; two are evaluated on the real robot. Our results show that this feature transform captures important aspects of walking and accelerates learning on hardware and simulation, as compared to traditional BO.

Connected through hyperedge HE-1 (induced by source paper "learning fast adaptation with meta strategy optimization"):
  with members:
    - "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "sim to real transfer for biped locomotion"
    - "norml no reward meta learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"

Connected through hyperedge HE-2 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning"):
  with members:
    - "riemannian motion policies"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "deep kernels for optimizing locomotion controllers"
    - "deep reinforcement learning a brief survey"
    - "on the design of lqr kernels for efficient controller learning"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "learning dynamic robot to human object handover from human feedback"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"

Connected through hyperedge HE-3 (induced by source paper "robust humanoid locomotion using trajectory optimization and sample efficient learning"):
  with members:
    - "locomotion planning through a hybrid bayesian trajectory optimization"
    - "differential dynamic programming for multi phase rigid contact dynamics"
    - "walking control based on step timing adaptation"
    - "pattern generation for walking on slippery terrains"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "sample efficient optimization for learning controllers for bipedal locomotion"
    - "robots that can adapt like animals"
    - "trajectory generation for multi contact momentum control"

Connected through hyperedge HE-4 (induced by source paper "a learnable safety measure"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "feedback control for cassie with deep reinforcement learning"
    - "real time control for autonomous racing based on viability theory"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "safe exploration in markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "classified regression for bayesian optimization robot learning with unknown penalties"):
  with members:
    - "gait learning for soft microrobots controlled by light fields"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-6 (induced by source paper "locomotion planning through a hybrid bayesian trajectory optimization"):
  with members:
    - "learning a structured neural network policy for a hopping task"
    - "sequential linear quadratic optimal control for nonlinear switched systems"
    - "reactive planar manipulation with convex hybrid mpc"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"

Connected through hyperedge HE-7 (induced by source paper "trajectory optimization for robust humanoid locomotion with sample efficient learning"):
  with members:
    - "pattern generation for walking on slippery terrains"

Connected through hyperedge HE-8 (induced by source paper "learning to walk via deep reinforcement learning"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "policies modulating trajectory generators"
    - "value constrained model free continuous control"
    - "composable deep reinforcement learning for robotic manipulation"
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "reward constrained policy optimization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "soft actor critic algorithms and applications"

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)