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: "efficient informative sensing using multiple robots"
  Abstract: The need for efficient monitoring of spatio-temporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, these robots have bounded resources, such as limited battery or limited amounts of time to obtain measurements. Thus, careful coordination of their paths is required in order to maximize the amount of information collected, while respecting the resource constraints. In this paper, we present an efficient approach for near-optimally solving the NP-hard optimization problem of planning such informative paths. In particular, we first develop eSIP (efficient Single-robot Informative Path planning), an approximation algorithm for optimizing the path of a single robot. Hereby, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to quantify the amount of information collected. We prove that the mutual information collected using paths obtained by using eSIP is close to the information obtained by an optimal solution. We then provide a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multi-robot problem. This procedure approximately generalizes any guarantees for the single-robot problem to the multi-robot case. We extensively evaluate the effectiveness of our approach on several experiments performed infield for two important environmental sensing applications, lake and river monitoring, and simulation experiments performed using several real world sensor network data sets.

Connected through hyperedge HE-1 (induced by source paper "learning q network for active information acquisition"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "real time visual tracking by deep reinforced decision making"
    - "bridging the gap between value and policy based reinforcement learning"
    - "end to end active object tracking via reinforcement learning"
    - "deep reinforcement learning for visual object tracking in videos"
    - "assumed density filtering q learning"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "search and rescue under the forest canopy using multiple uavs"):
  with members:
    - "segmap 3d segment mapping using data driven descriptors"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "data efficient decentralized visual slam"
    - "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"
    - "dynamic clustering via asymptotics of the dependent dirichlet process mixture"

Connected through hyperedge HE-3 (induced by source paper "decentralized multi target tracking in urban environments overview and challenges"):
  with members:
    - "towards search based motion planning for micro aerial vehicles"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "sensor selection for target tracking in wireless sensor networks with uncertainty"
    - "the cauchy schwarz divergence for poisson point processes"
    - "information acquisition with sensing robots algorithms and error bounds"
    - "cooperative multi target localization with noisy sensors"
    - "point based pomdp algorithms improved analysis and implementation"

Connected through hyperedge HE-4 (induced by source paper "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"):
  with members:
    - "warped gaussian processes occupancy mapping with uncertain inputs"
    - "gaussian process autonomous mapping and exploration for range sensing mobile robots"
    - "persistent monitoring of events with stochastic arrivals at multiple stations"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "energy efficient path planning for ground robots by combining air and ground measurements"):
  with members:
    - "estimating achievable range of ground robots operating on single battery discharge for operational efficacy amelioration"
    - "learning ground traversability from simulations"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "riverine coverage with an autonomous surface vehicle over known environments"):
  with members:
    - "multi robot dubins coverage with autonomous surface vehicles"
    - "an autonomous surface vehicle for long term operations"
    - "external force field modeling for autonomous surface vehicles"
    - "efficient multi robot coverage of a known environment"

Connected through hyperedge HE-7 (induced by source paper "online exploration of an unknown region of interest with a team of aerial robots"):
  with members:
    - "a competitive algorithm for online multi robot exploration of a translating plume"

Connected through hyperedge HE-8 (induced by source paper "an informative path planning framework for uav based terrain monitoring"):
  with members:
    - "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"
    - "hindsight is only 50 50 unsuitability of mdp based approximate pomdp solvers for multi resolution information gathering"
    - "a data driven approach for autonomous motion planning and control in off road driving scenarios"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "multiresolution mapping and informative path planning for uav based terrain monitoring"
    - "dynamic system identification and control for a cost effective open source vtol mav"
    - "weednet dense semantic weed classification using multispectral images and mav for smart farming"
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"

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)