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: "distributed state estimation using intermittently connected robot networks"
  Abstract: This paper considers the problem of distributed state estimation (DSE) using multirobot systems. The robots have limited communication capabilities and, therefore, communicate their measurements intermittently only when they are physically close to each other. To decrease the distance that the robots need to travel only to communicate, we divide them into small teams that can communicate at different locations to share information and update their beliefs. Then, we propose a new distributed scheme that combines: first, communication schedules that ensure that the network is intermittently connected, and second, sampling-based motion planning for the robots in every team with the objective to collect optimal measurements and decide a location for those robots to communicate. To the best of our knowledge, this is the first DSE framework that relaxes all network connectivity assumptions, and controls intermittent communication events so that the estimation uncertainty is minimized. We present simulation results that demonstrate significant improvement in estimation accuracy compared to methods that maintain an end-to-end connected network for all time.

Connected through hyperedge HE-1 (induced by source paper "distributed attack robust submodular maximization for multi robot planning"):
  with members:
    - "the impact of information in distributed submodular maximization"
    - "robust monotone submodular function maximization"
    - "resilient active information gathering with mobile robots"
    - "resilient active target tracking with multiple robots"
    - "distributed submodular maximization with limited information"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "fast algorithms for the maximum clique problem on massive sparse graphs"
    - "high speed flight in an ergodic forest"

Connected through hyperedge HE-2 (induced by source paper "varnet variational neural networks for the solution of partial differential equations"):
  with members:
    - "model based active source identification in complex environments"
    - "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-3 (induced by source paper "model based active source identification in complex environments"):
  with members:
    - "ergodic exploration of distributed information"
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"

Connected through hyperedge HE-4 (induced by source paper "model based learning of turbulent flows using mobile robots"):
  with members:
    - "model based active source identification in complex environments"
    - "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 "sensor assignment algorithms to improve observability while tracking targets"):
  with members:
    - "trackerbots autonomous unmanned aerial vehicle for real time localization and tracking of multiple radio tagged animals"
    - "trackerbots autonomous unmanned aerial vehicle for real time localization and tracking of multiple radio tagged animals"
    - "active target tracking with self triggered communications in multi robot teams"
    - "distributed algorithms for feature extraction off loading in multi camera visual sensor networks"
    - "observability aware trajectory optimization for self calibration with application to uavs"
    - "scheduling nonlinear sensors for stochastic process estimation"

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)