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: "knowledge transfer for scene specific motion prediction"
  Abstract: When given a single frame of the video, humans can not only interpret the content of the scene, but also they are able to forecast the near future. This ability is mostly driven by their rich prior knowledge about the visual world, both in terms of (i) the dynamics of moving agents, as well as (ii) the semantic of the scene. In this work we exploit the interplay between these two key elements to predict scene-specific motion patterns. First, we extract patch descriptors encoding the probability of moving to the adjacent patches, and the probability of being in that particular patch or changing behavior. Then, we introduce a Dynamic Bayesian Network which exploits this scene specific knowledge for trajectory prediction. Experimental results demonstrate that our method is able to accurately predict trajectories and transfer predictions to a novel scene characterized by similar elements.

Connected through hyperedge HE-1 (induced by source paper "ego pose estimation and forecasting as real time pd control"):
  with members:
    - "xr egopose egocentric 3d human pose from an hmd camera"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning 3d human dynamics from video"
    - "sfv reinforcement learning of physical skills from videos"
    - "flow grounded spatial temporal video prediction from still images"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "unsupervised learning of disentangled representations from video"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-2 (induced by source paper "diverse trajectory forecasting with determinantal point processes"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "learning 3d human dynamics from video"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "learning detection with diverse proposals"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "creating capsule wardrobes from fashion images"
    - "deep representation learning for human motion prediction and classification"

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"
    - "context aware trajectory prediction"
    - "attention is all you need"
    - "desire distant future prediction in dynamic scenes with interacting agents"

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 "understanding pedestrian vehicle interactions with vehicle mounted vision an lstm model and empirical analysis"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "car net clairvoyant attentive recurrent network"

Connected through hyperedge HE-6 (induced by source paper "deep context map agent trajectory prediction using location specific latent maps"):
  with members:
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "context aware pedestrian motion prediction in urban intersections"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "car net clairvoyant attentive recurrent network"
    - "pedestrian prediction by planning using deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "social and scene aware trajectory prediction in crowded spaces"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "transferable pedestrian motion prediction models at intersections"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "car net clairvoyant attentive recurrent network"
    - "social attention modeling attention in human crowds"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-8 (induced by source paper "trajectory prediction by coupling scene lstm with human movement lstm"):
  with members:
    - "spatiotemporal ksvd dictionary learning for online multi target tracking"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "context aware trajectory prediction"
    - "social attention modeling attention in human crowds"
    - "human trajectory prediction using spatially aware deep attention models"
    - "adam a method for stochastic optimization"

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)