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: "deep learning driven visual path prediction from a single image"
  Abstract: Capabilities of inference and prediction are the significant components of visual systems. Visual path prediction is an important and challenging task among them, with the goal to infer the future path of a visual object in a static scene. This task is complicated as it needs high-level semantic understandings of both the scenes and underlying motion patterns in video sequences. In practice, cluttered situations have also raised higher demands on the effectiveness and robustness of models. Motivated by these observations, we propose a deep learning framework, which simultaneously performs deep feature learning for visual representation in conjunction with spatiotemporal context modeling. After that, a unified path-planning scheme is proposed to make accurate path prediction based on the analytic results returned by the deep context models. The highly effective visual representation and deep context models ensure that our framework makes a deep semantic understanding of the scenes and motion patterns, consequently improving the performance on visual path prediction task. In experiments, we extensively evaluate the model’s performance by constructing two large benchmark datasets from the adaptation of video tracking datasets. The qualitative and quantitative experimental results show that our approach outperforms the state-of-the-art approaches and owns a better generalization capability.

Connected through hyperedge HE-1 (induced by source paper "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "learning what you can do before doing anything"
    - "flow grounded spatial temporal video prediction from still images"
    - "action anticipation by predicting future dynamic images"

Connected through hyperedge HE-2 (induced by source paper "conditional vehicle trajectories prediction in carla urban environment"):
  with members:
    - "convolutional social pooling for vehicle trajectory prediction"
    - "conditional affordance learning for driving in urban environments"
    - "deep imitative models for flexible inference planning and control"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "end to end vehicle lateral control using a single fisheye camera"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-3 (induced by source paper "forecasting people trajectories and head poses by jointly reasoning on tracklets and vislets"):
  with members:
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "3dof pedestrian trajectory prediction learned from long term autonomous mobile robot deployment data"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "human trajectory prediction using spatially aware deep attention models"
    - "long short term memory kalman filters recurrent neural estimators for pose regularization"

Connected through hyperedge HE-4 (induced by source paper "navigationnet a large scale interactive indoor navigation dataset"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "dual path networks"
    - "end to end learning of geometry and context for deep stereo regression"
    - "matterport3d learning from rgb d data in indoor environments"
    - "emergence of locomotion behaviours in rich environments"
    - "end to end learning of semantic grasping"

Connected through hyperedge HE-5 (induced by source paper "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"):
  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"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "on human motion prediction using recurrent neural networks"
    - "modeling cooperative navigation in dense human crowds"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "survey on vision based path prediction"):
  with members:
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "pedestrian prediction by planning using deep neural networks"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "knowledge transfer for scene specific motion prediction"
    - "forecasting interactive dynamics of pedestrians with fictitious play"
    - "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"

Connected through hyperedge HE-7 (induced by source paper "particle based pedestrian path prediction using lstm mdl models"):
  with members:
    - "state space lstm models with particle mcmc inference"
    - "context aware trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "effective approaches to attention based neural machine translation"
    - "a recurrent latent variable model for sequential data"
    - "neural adaptive sequential monte carlo"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "future person localization in first person videos"):
  with members:
    - "forecasting hand and object locations in future frames"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "unsupervised learning of depth and ego motion from video"
    - "next active object prediction from egocentric videos"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "forecasting interactive dynamics of pedestrians with fictitious play"
    - "realtime multi person 2d pose estimation using part affinity fields"

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)