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: "model based active source identification in complex environments"
  Abstract: In this paper, we consider the problem of Active Source Identification in steady-state advection–diffusion (AD) transport systems. Unlike existing bioinspired heuristic methods, we propose a model-based approach that employs the AD–partial differential equation (PDE) to capture the transport phenomenon. Specifically, we formulate the source identification (SI) problem as a PDE-constrained optimization problem in function spaces. To obtain a tractable solution, we reduce the dimension of the concentration field using Proper Orthogonal Decomposition and approximate the unknown source field using nonlinear basis functions, drastically decreasing the number of unknowns. Moreover, to collect the concentration measurements, we control a robot sensor through a sequence of waypoints that maximize the smallest eigenvalue of the Fisher Information matrix of the unknown source parameters. Specifically, after every new measurement, an SI problem is solved to obtain a source estimate that is used to determine the next waypoint. We show that our algorithm can efficiently identify sources in complex AD systems and nonconvex domains, in simulation and experimentally. This is the first time that PDEs are used for robotic SI in practice.

Connected through hyperedge HE-1 (induced by source paper "varnet variational neural networks for the solution of partial differential equations"):
  with members:
    - "distributed state estimation using intermittently connected robot networks"
    - "deep learning for robotic mass transport cloaking"
    - "general solutions for nonlinear differential equations a deep reinforcement learning approach"
    - "model based learning of turbulent flows using mobile robots"
    - "neural networks catching up with finite differences in solving partial differential equations in higher dimensions"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "distributed state estimation using intermittently connected robot networks"):
  with members:
    - "distributed active state estimation with user specified accuracy"
    - "distributed hierarchical control for state estimation with robotic sensor networks"
    - "simultaneous intermittent communication control and path optimization in networks of mobile robots"
    - "information acquisition with sensing robots algorithms and error bounds"
    - "decentralized estimation of laplacian eigenvalues in multi agent systems"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "a successive elimination approach to adaptive robotic source seeking"):
  with members:
    - "multi armed bandits with metric movement costs"
    - "ergodic exploration of distributed information"
    - "best of k bandits"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "information acquisition with sensing robots algorithms and error bounds"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-4 (induced by source paper "model based learning of turbulent flows using mobile robots"):
  with members:
    - "distributed state estimation using intermittently connected robot networks"
    - "control of uniflagellar soft robots at low reynolds number using buckling instability"
    - "distributed active state estimation with user specified accuracy"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"

Connected through hyperedge HE-5 (induced by source paper "deep learning for robotic mass transport cloaking"):
  with members:
    - "varnet variational neural networks for the solution of partial differential equations"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"

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)