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: "understanding human behaviors in crowds by imitating the decision making process"
  Abstract: Crowd behavior understanding is crucial yet challenging across a wide range of applications, since crowd behavior is inherently determined by a sequential decision-making process based on various factors, such as the pedestrians' own destinations, interaction with nearby pedestrians and anticipation of upcoming events. In this paper, we propose a novel framework of Social-Aware Generative Adversarial Imitation Learning (SA-GAIL) to mimic the underlying decision-making process of pedestrians in crowds. Specifically, we infer the latent factors of human decision-making process in an unsupervised manner by extending the Generative Adversarial Imitation Learning framework to anticipate future paths of pedestrians. Different factors of human decision making are disentangled with mutual information maximization, with the process modeled by collision avoidance regularization and Social-Aware LSTMs. Experimental results demonstrate the potential of our framework in disentangling the latent decision-making factors of pedestrians and stronger abilities in predicting future trajectories.

Connected through hyperedge HE-1 (induced by source paper "learning to simulate human movement"):
  with members:
    - "social and scene aware trajectory prediction in crowded spaces"
    - "peeking into the future predicting future person activities and locations in videos"
    - "inferring metapopulation propagation network for intra city epidemic control and prevention"
    - "multi agent imitation learning for driving simulation"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "generative adversarial imitation learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "trust region policy optimization"

Connected through hyperedge HE-2 (induced by source paper "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "convolutional sequence to sequence model for human dynamics"
    - "action agnostic human pose forecasting"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-3 (induced by source paper "peeking into the future predicting future person activities and locations in videos"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "a transferable pedestrian motion prediction model for intersections with different geometries"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "scene lstm a model for human trajectory prediction"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "relation networks for object detection"
    - "future person localization in first person videos"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-4 (induced by source paper "stochastic trajectory prediction with social graph network"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "stochastic variational video prediction"

Connected through hyperedge HE-5 (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"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "context aware trajectory prediction"
    - "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-6 (induced by source paper "hierarchical reinforcement learning framework towards multi agent navigation"):
  with members:
    - "learning and inferring a driver s braking action in car following scenarios"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "hindsight experience replay"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "overview a hierarchical framework for plan generation and execution in multirobot systems"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"

Connected through hyperedge HE-7 (induced by source paper "pedestrian trajectory prediction with structured memory hierarchies"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "learning temporal strategic relationships using generative adversarial imitation learning"
    - "efficient large scale multi modal classification"
    - "context aware trajectory prediction"
    - "neural map structured memory for deep reinforcement learning"
    - "tree memory networks for modelling long term temporal dependencies"
    - "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)