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: "neural network model for path planning of robotic rover systems"
  Abstract: Today, robotics is an auspicious and fast-growing branch of technology that involves the manufacturing, design, and maintenance of robot machines that can operate in an autonomous fashion and can be used in a wide variety of applications including space exploration, weaponry, household, and transportation. More particularly, in space applications, a common type of robots has been of widespread use in the recent years. It is called planetary rover which is a robot vehicle that moves across the surface of a planet and conducts detailed geological studies pertaining to the properties of the landing cosmic environment. However, rovers are always impeded by obstacles along the traveling path which can destabilize the rover’s body and prevent it from reaching its goal destination. This paper proposes an ANN model that allows rover systems to carry out autonomous path-planning to successfully navigate through challenging planetary terrains and follow their goal location while avoiding dangerous obstacles. The proposed ANN is a multilayer network made out of three layers: an input, a hidden, and an output layer. The network is trained in offline mode using back-propagation supervised learning algorithm. A software-simulated rover was experimented and it revealed that it was able to follow the safest trajectory despite existing obstacles. As future work, the proposed ANN is to be parallelized so as to speed-up the execution time of the training process.

Connected through hyperedge HE-1 (induced by source paper "a novel deep neural network architecture for mars visual navigation"):
  with members:
    - "deep reinforcement learning an overview"
    - "memory augmented control networks"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "value iteration networks"
    - "recent advances in convolutional neural networks"
    - "deep residual learning for image recognition"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-2 (induced by source paper "a novel learning based global path planning algorithm for planetary rovers"):
  with members:
    - "cognitive mapping and planning for visual navigation"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "value iteration networks"
    - "deep residual learning for image recognition"
    - "end to end training of deep visuomotor policies"

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.NE(Neural and Evolutionary Computing)