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: "realtime multilevel crowd tracking using reciprocal velocity obstacles"
  Abstract: We present a novel, realtime algorithm to compute the trajectory of each pedestrian in moderately dense crowd scenes. Our formulation is based on an adaptive particle filtering scheme that uses a multi-agent motion model based on velocity-obstacles, and takes into account local interactions as well as physical and personal constraints of each pedestrian. Our method dynamically changes the number of particles allocated to each pedestrian based on different confidence metrics. Additionally, we use a new high-definition crowd video dataset, which is used to evaluate the performance of different pedestrian tracking algorithms. This dataset consists of videos of indoor and outdoor scenes, recorded at different locations with 30-80 pedestrians. We highlight the performance benefits of our algorithm over prior techniques using this dataset. In practice, our algorithm can compute trajectories of tens of pedestrians on a multi-core desktop CPU at interactive rates (27-30 frames per second). To the best of our knowledge, our approach is 4-5 times faster than prior methods, which provide similar accuracy.

Connected through hyperedge HE-1 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors"):
  with members:
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "yolov3 an incremental improvement"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"

Connected through hyperedge HE-2 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "low shot learning from imaginary 3d model"
    - "self paced deep learning for weakly supervised object detection"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "experience replay for continual learning"
    - "hierarchy of gans for learning embodied self awareness model"
    - "a multi perspective approach to anomaly detection for self aware embodied agents"
    - "foil it find one mismatch between image and language caption"

Connected through hyperedge HE-3 (induced by source paper "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"):
  with members:
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "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"
    - "fusion of head and full body detectors for multi object tracking"
    - "real time multiple object tracking a study on the importance of speed"
    - "simple online and realtime tracking with a deep association metric"
    - "recurrent autoregressive networks for online multi object tracking"

Connected through hyperedge HE-4 (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"
    - "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"

Connected through hyperedge HE-5 (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"
    - "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"
    - "a hybrid data association framework for robust online multi object tracking"
    - "simple online and realtime tracking with a deep association metric"

Connected through hyperedge HE-6 (induced by source paper "the emotionally intelligent robot improving social navigation in crowded environments"):
  with members:
    - "deep learning for human affect recognition insights and new developments"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "crowdmove autonomous mapless navigation in crowded scenarios"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"
    - "social attention modeling attention in human crowds"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-7 (induced by source paper "personality aware probabilistic map for trajectory prediction of pedestrians"):
  with members:
    - "situation aware pedestrian trajectory prediction with spatio temporal attention model"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "towards binary valued gates for robust lstm training"
    - "an lstm network for highway trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "crowd behavior simulation with emotional contagion in unexpected multi hazard situations"
    - "context aware trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-8 (induced by source paper "the role of data driven priors in multi agent crowd trajectory estimation"):
  with members:
    - "crowded scene analysis a survey"
    - "a message passing algorithm for multi agent trajectory planning"

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.CV(Computer Vision and Pattern Recognition)