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: "context aware trajectory prediction"
  Abstract: Human motion and behaviour in crowded spaces is influenced by several factors, such as the dynamics of other moving agents in the scene, as well as the static elements that might be perceived as points of attraction or obstacles. In this work, we present a new model for human trajectory prediction which is able to take advantage of both human-human and human-space interactions. The future trajectory of humans, are generated by observing their past positions and interactions with the surroundings. To this end, we propose a "context-aware" recurrent neural network LSTM model, which can learn and predict human motion in crowded spaces such as a sidewalk, a museum or a shopping mall. We evaluate our model on a public pedestrian datasets, and we contribute a new challenging dataset that collects videos of humans that navigate in a (real) crowded space such as a big museum. Results show that our approach can predict human trajectories better when compared to previous state-of-the-art forecasting models.

Connected through hyperedge HE-1 (induced by source paper "mcenet multi context encoder network for homogeneous agent trajectory prediction in mixed traffic"):
  with members:
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "scene lstm a model for human trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "precog prediction conditioned on goals in visual multi agent settings"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "an algorithmic perspective on imitation learning"
    - "deep imitative models for flexible inference planning and control"
    - "generative modeling of multimodal multi human behavior"
    - "generative multi agent behavioral cloning"

Connected through hyperedge HE-3 (induced by source paper "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"):
  with members:
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "real world anomaly detection in surveillance videos"
    - "particle based pedestrian path prediction using lstm mdl models"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "attention is all you need"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "tree memory networks for modelling long term temporal dependencies"

Connected through hyperedge HE-4 (induced by source paper "what the constant velocity model can teach us about pedestrian motion prediction"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "a transferable pedestrian motion prediction model for intersections with different geometries"
    - "context aware pedestrian motion prediction in urban intersections"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "autonomous vehicles that interact with pedestrians a survey of theory and practice"

Connected through hyperedge HE-5 (induced by source paper "situation aware pedestrian trajectory prediction with spatio temporal attention model"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "attention is all you need"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "social attention modeling attention in human crowds"
    - "human trajectory prediction using spatially aware deep attention models"
    - "temporal dynamic graph lstm for action driven video object detection"

Connected through hyperedge HE-6 (induced by source paper "predicting vehicle behaviors over an extended horizon using behavior interaction network"):
  with members:
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "on a formal model of safe and scalable self driving cars"

Connected through hyperedge HE-7 (induced by source paper "locality constrained spatial transformer network for video crowd counting"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"
    - "spatiotemporal modeling for crowd counting in videos"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "toward end to end face recognition through alignment learning"

Connected through hyperedge HE-8 (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"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"

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)