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: "human action recognition and prediction a survey"
  Abstract: Derived from rapid advances in computer vision and machine learning, video analysis tasks have been moving from inferring the present state to predicting the future state. Vision-based action recognition and prediction from videos are such tasks, where action recognition is to infer human actions (present state) based upon complete action executions, and action prediction to predict human actions (future state) based upon incomplete action executions. These two tasks have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as visual surveillance, autonomous driving vehicle, entertainment, and video retrieval, etc. Many attempts have been devoted in the last a few decades in order to build a robust and effective framework for action recognition and prediction. In this paper, we survey the complete state-of-the-art techniques in the action recognition and prediction. Existing models, popular algorithms, technical difficulties, popular action databases, evaluation protocols, and promising future directions are also provided with systematic discussions.

Connected through hyperedge HE-1 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation"):
  with members:
    - "temporal attentive alignment for video domain adaptation"
    - "temporal attentive alignment for large scale video domain adaptation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "attending to discriminative certainty for domain adaptation"
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "multi adversarial domain adaptation"
    - "self supervised video representation learning with space time cubic puzzles"

Connected through hyperedge HE-2 (induced by source paper "human action performance using deep neuro fuzzy recurrent attention model"):
  with members:
    - "a fine to coarse convolutional neural network for 3d human action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "action attending graphic neural network"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "attention is all you need"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "zero shot activity recognition with videos"):
  with members:
    - "unseen action recognition with multimodal learning"
    - "jointly localizing and describing events for dense video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint event detection and description in continuous video streams"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "zero shot activity recognition with verb attribute induction"
    - "the kinetics human action video dataset"
    - "tall temporal activity localization via language query"

Connected through hyperedge HE-4 (induced by source paper "image based ood detector principles on graph based input data in human action recognition"):
  with members:
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "metric learning for novelty and anomaly detection"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "novelty detection with gan"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "stacked hourglass networks for human pose estimation"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-5 (induced by source paper "synthetic humans for action recognition from unseen viewpoints"):
  with members:
    - "temporally coherent full 3d mesh human pose recovery from monocular video"
    - "hierarchically learned view invariant representations for cross view action recognition"
    - "learning joint reconstruction of hands and manipulated objects"
    - "learning to train with synthetic humans"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "virtualhome simulating household activities via programs"
    - "learning to estimate 3d human pose and shape from a single color image"

Connected through hyperedge HE-6 (induced by source paper "pisep 2 pseudo image sequence evolution based 3d pose prediction"):
  with members:
    - "frequency domain transformer networks for video prediction"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deep video generation prediction and completion of human action sequences"
    - "stochastic adversarial video prediction"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "timeception for complex action recognition"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"
    - "learning to decompose and disentangle representations for video prediction"

Connected through hyperedge HE-7 (induced by source paper "zero shot action recognition in videos a survey"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "fine grained activity recognition in baseball videos"
    - "towards universal representation for unseen action recognition"
    - "a short note about kinetics 600"
    - "graph convolutional networks for text classification"
    - "visual data synthesis via gan for zero shot video classification"
    - "cross modal and hierarchical modeling of video and text"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-8 (induced by source paper "dmcl distillation multiple choice learning for multimodal action recognition"):
  with members:
    - "unifying heterogeneous classifiers with distillation"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "snapshot distillation teacher student optimization in one generation"
    - "action recognition for depth video using multi view dynamic images"
    - "motion feature network fixed motion filter for action recognition"
    - "modality distillation with multiple stream networks for action recognition"
    - "representation flow for action recognition"

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)