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: "predicting future instance segmentations by forecasting convolutional features"
  Abstract: Anticipating future events is an important prerequisite towards intelligent behavior. Video forecasting has been studied as a proxy task towards this goal. Recent work has shown that to predict semantic segmentation of future frames, forecasting at the semantic level is more effective than forecasting RGB frames and then segmenting these. In this paper we consider the more challenging problem of future instance segmentation, which additionally segments out individual objects. To deal with a varying number of output labels per image, we develop a predictive model in the space of fixed-sized convolutional features of the Mask R-CNN instance segmentation model. We apply the "detection head" of Mask R-CNN on the predicted features to produce the instance segmentation of future frames. Experiments show that this approach significantly improves over baselines based on optical flow.

Connected through hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data"):
  with members:
    - "a short note on the kinetics 700 human action dataset"
    - "large scale adversarial representation learning"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarial video generation on complex datasets"
    - "scaling autoregressive video models"
    - "object oriented dynamics predictor"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "sequential attend infer repeat generative modelling of moving objects"

Connected through hyperedge HE-2 (induced by source paper "modeling human motion with quaternion based neural networks"):
  with members:
    - "quaternion convolutional neural networks"
    - "a neural temporal model for human motion prediction"
    - "human motion modeling using dvgans"
    - "quaternet a quaternion based recurrent model for human motion"
    - "convolutional sequence to sequence model for human dynamics"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "neural kinematic networks for unsupervised motion retargetting"

Connected through hyperedge HE-3 (induced by source paper "learning multi object tracking and segmentation from automatic annotations"):
  with members:
    - "video instance segmentation"
    - "disentangling monocular 3d object detection"
    - "seamless scene segmentation"
    - "mots multi object tracking and segmentation"
    - "panoptic deeplab"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "combined image and world space tracking in traffic scenes"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-4 (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"
    - "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"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "segmenting the future"):
  with members:
    - "action agnostic human pose forecasting"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "bayesian prediction of future street scenes using synthetic likelihoods"
    - "context encoding for semantic segmentation"
    - "d3d distilled 3d networks for video action recognition"
    - "future semantic segmentation with convolutional lstm"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "predicting deeper into the future of semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "single level feature to feature forecasting with deformable convolutions"):
  with members:
    - "panoptic segmentation"
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "efficient ladder style densenets for semantic segmentation of large images"
    - "recurrent flow guided semantic forecasting"
    - "deformable convnets v2 more deformable better results"
    - "bayesian prediction of future street scenes using synthetic likelihoods"
    - "predicting deeper into the future of semantic segmentation"
    - "deformable convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "learning representations for predicting future activities"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "stochastic adversarial video prediction"
    - "when will you do what anticipating temporal occurrences of activities"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "predicting action tubes"
    - "predicting deeper into the future of semantic segmentation"
    - "on calibration of modern neural networks"
    - "contextvp fully context aware video prediction"

Connected through hyperedge HE-8 (induced by source paper "short term prediction and multi camera fusion on semantic grids"):
  with members:
    - "monocular plan view networks for autonomous driving"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "long term occupancy grid prediction using recurrent neural networks"
    - "deep network uncertainty maps for indoor navigation"
    - "predicting deeper into the future of semantic segmentation"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "long term on board prediction of people in traffic scenes under uncertainty"

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)