human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "real time multi target path prediction and planning for autonomous driving aided by fcn"):
  Abstract: Real-time multi-target path planning is a key issue in the field of autonomous driving. Although multiple paths can be generated in real-time with polynomial curves, the generated paths are not flexible enough to deal with complex road scenes such as S-shaped road and unstructured scenes such as parking lots. Search and sampling-based methods, such as A* and RRT and their derived methods, are flexible in generating paths for these complex road environments. However, the existing algorithms require significant time to plan to multiple targets, which greatly limits their application in autonomous driving. In this paper, a real-time path planning method for multi-targets is proposed. We train a fully convolutional neural network (FCN) to predict a path region for the target at first. By taking the predicted path region as soft constraints, the A* algorithm is then applied to search the exact path to the target. Experiments show that FCN can make multiple predictions in a very short time (50 times in 40ms), and the predicted path region effectively restrict the searching space for the following A* search. Therefore, the A* can search much faster so that the multi-target path planning can be achieved in real-time (3 targets in less than 100ms).

Includes members:
  Member "learning lightweight lane detection cnns by self attention distillation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "bag of tricks for image classification with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "tiev the tongji intelligent electric vehicle in the intelligent vehicle future challenge of china":
    also connected through other hyperedges:
      (none)
  Member "dual attention network for scene segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "lidar based driving path generation using fully convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of deep learning applications to autonomous vehicle control")
  Member "enet a deep neural network architecture for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors")
  Member "sampling based algorithms for optimal motion planning using closed loop prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast and accurate mapping for autonomous racing")

Question: Please tell me which class the source paper 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.AI(Artificial Intelligence)