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: "hopc histogram of oriented principal components of 3d pointclouds for action recognition"
  Abstract: Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which change significantly with viewpoint. In contrast, we directly process the pointclouds and propose a new technique for action recognition which is more robust to noise, action speed and viewpoint variations. Our technique consists of a novel descriptor and keypoint detection algorithm. The proposed descriptor is extracted at a point by encoding the Histogram of Oriented Principal Components (HOPC) within an adaptive spatio-temporal support volume around that point. Based on this descriptor, we present a novel method to detect Spatio-Temporal Key-Points (STKPs) in 3D pointcloud sequences. Experimental results show that the proposed descriptor and STKP detector outperform state-of-the-art algorithms on three benchmark human activity datasets. We also introduce a new multiview public dataset and show the robustness of our proposed method to viewpoint variations.

Connected through hyperedge HE-1 (induced by source paper "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "video representation learning using discriminative pooling"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "sphereface deep hypersphere embedding for face recognition"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "a comparative review of recent kinect based action recognition algorithms"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "spatio temporal graph convolution for skeleton based action recognition"

Connected through hyperedge HE-3 (induced by source paper "rsa randomized simulation as augmentation for robust human action recognition"):
  with members:
    - "meta sim learning to generate synthetic datasets"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "virtualhome simulating household activities via programs"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-4 (induced by source paper "ev action electromyography vision multi modal action dataset"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"
    - "pku mmd a large scale benchmark for continuous multi modal human action understanding"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "action recognition based on joint trajectory maps using convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "predict cluster unsupervised skeleton based action recognition"):
  with members:
    - "clustering and recognition of spatiotemporal features through interpretable embedding of sequence to sequence recurrent neural networks"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "unsupervised learning of view invariant action representations"
    - "unsupervised learning of long term motion dynamics for videos"
    - "on human motion prediction using recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "loss switching fusion with similarity search for video classification"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "attention clusters purely attention based local feature integration for video classification"
    - "improved dense trajectory with cross streams"
    - "a survey on learning to hash"
    - "fast video classification via adaptive cascading of deep models"
    - "holistic features for real time crowd behaviour anomaly detection"

Connected through hyperedge HE-7 (induced by source paper "action recognition for depth video using multi view dynamic images"):
  with members:
    - "rgb d based human motion recognition with deep learning a survey"
    - "rank pooling for action recognition"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "deep learning on lie groups for skeleton based action recognition"
    - "action recognition with dynamic image networks"

Connected through hyperedge HE-8 (induced by source paper "human activity recognition in rgb d videos by dynamic images"):
  with members:
    - "rgb d based human motion recognition with deep learning a survey"
    - "cooperative training of deep aggregation networks for rgb d action recognition"
    - "rank pooling for action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "kinematic layout aware random forests for depth based action recognition"
    - "action recognition with dynamic image networks"
    - "very deep convolutional networks for large scale image 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)