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 reciprocal collision avoidance between heterogeneous agents using ctmat"
  Abstract: We present a novel algorithm for reciprocal collision avoidance between heterogeneous agents of different shapes and sizes. We present a novel CTMAT representation based on medial axis transform to compute a tight fitting bounding shape for each agent. Each CTMAT is represented using tuples, which are composed of circular arcs and line segments. Based on the reciprocal velocity obstacle formulation, we reduce the problem to solving a low-dimensional linear programming between each pair of tuples belonging to adjacent agents. We precompute the Minkowski Sums of tuples to accelerate the runtime performance. Finally, we provide an efficient method to update the orientation of each agent in a local manner. We have implemented the algorithm and highlight its performance on benchmarks corresponding to road traffic scenarios and different vehicles. The overall runtime performance is comparable to prior multi-agent collision avoidance algorithms that use circular or elliptical agents. Our approach is less conservative and results in fewer false collisions.

Connected through hyperedge HE-1 (induced by source paper "gamma a general agent motion prediction model for autonomous driving"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "an lstm network for highway trajectory prediction"

Connected through hyperedge HE-2 (induced by source paper "roadtrack realtime tracking of road agents in dense and heterogeneous environments"):
  with members:
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "a hybrid data association framework for robust online multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "roadtrack tracking road agents in dense and heterogeneous environments"):
  with members:
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "a hybrid data association framework for robust online multi object tracking"
    - "simple online and realtime tracking with a deep association metric"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"

Connected through hyperedge HE-4 (induced by source paper "nh ttc a gradient based framework for generalized anticipatory collision avoidance"):
  with members:
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"):
  with members:
    - "identifying driver behaviors using trajectory features for vehicle navigation"
    - "autonomous driving among many pedestrians models and algorithms"
    - "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints"
    - "carla an open urban driving simulator"

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.AI(Artificial Intelligence)