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: "long short term memory kalman filters recurrent neural estimators for pose regularization"
  Abstract: One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization. One of the most widely-used methods is the Kalman filter, which is both extremely simple and general. However, Kalman filters require a motion model and measurement model to be specified a priori, which burdens the modeler and simultaneously demands that we use explicit models that are often only crude approximations of reality. For example, in the pose-estimation tasks mentioned above, it is common to use motion models that assume constant velocity or constant acceleration, and we believe that these simplified representations are severely inhibitive. In this work, we propose to instead learn rich, dynamic representations of the motion and noise models. In particular, we propose learning these models from data using long short term memory, which allows representations that depend on all previous observations and all previous states. We evaluate our method using three of the most popular pose estimation tasks in computer vision, and in all cases we obtain state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "the apolloscape open dataset for autonomous driving and its application"):
  with members:
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deepmvs learning multi view stereopsis"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-2 (induced by source paper "trajectory space factorization for deep video based 3d human pose estimation"):
  with members:
    - "semantic graph convolutional networks for 3d human pose regression"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "3d human pose estimation in the wild by adversarial learning"
    - "exploiting temporal information for 3d human pose estimation"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "wav2letter an end to end convnet based speech recognition system"
    - "compositional human pose regression"
    - "a simple yet effective baseline for 3d human pose estimation"

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"
    - "deep learning driven visual path prediction from a single image"

Connected through hyperedge HE-4 (induced by source paper "interaction aware kalman neural networks for trajectory prediction"):
  with members:
    - "convolutional social pooling for vehicle trajectory prediction"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "baidu apollo em motion planner"
    - "multi agent imitation learning for driving simulation"
    - "imitating driver behavior with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "multivariate uncertainty in deep learning"):
  with members:
    - "real time object pose estimation with pose interpreter networks"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deepvo a deep learning approach for monocular visual odometry"
    - "backprop kf learning discriminative deterministic state estimators"
    - "deep residual learning for image recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "inertial single vehicle trajectory prediction baselines and applications with the ngsim dataset"):
  with members:
    - "interaction aware kalman neural networks for trajectory prediction"
    - "on the variance of the adaptive learning rate and beyond"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "lookahead optimizer k steps forward 1 step back"
    - "convolutional social pooling for vehicle trajectory prediction"

Connected through hyperedge HE-7 (induced by source paper "learning 3d human dynamics from video"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "reticam real time human performance capture from monocular video"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "deep autoencoder for combined human pose estimation and body model upscaling"

Connected through hyperedge HE-8 (induced by source paper "dels 3d deep localization and segmentation with a 3d semantic map"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "a survey of augmented reality navigation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "semantic video cnns through representation warping"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "demon depth and motion network for learning monocular stereo"
    - "playing for benchmarks"

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)