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: "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
  Abstract: Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding. [The dataset is available at: this http URL]

Connected through hyperedge HE-1 (induced by source paper "a survey on 3d skeleton based action recognition using learning method"):
  with members:
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "make skeleton based action recognition model smaller faster and better"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "skelemotion a new representation of skeleton joint sequences based on motion information for 3d action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"

Connected through hyperedge HE-2 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition"):
  with members:
    - "learning class regularized features for action recognition"
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "real world anomaly detection in surveillance videos"
    - "sports camera calibration via synthetic data"
    - "pose flow efficient online pose tracking"
    - "memory attention networks for skeleton based action recognition"

Connected through hyperedge HE-3 (induced by source paper "feedback graph convolutional network for skeleton based action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "feedback network for image super resolution"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "interaction relational network for mutual action recognition"
    - "vertex feature encoding and hierarchical temporal modeling in a spatial temporal graph convolutional network for action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"

Connected through hyperedge HE-4 (induced by source paper "gimme signals discriminative signal encoding for multimodal activity recognition"):
  with members:
    - "joint activity recognition and indoor localization with wifi fingerprints"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "mfas multimodal fusion architecture search"
    - "simitate a hybrid imitation learning benchmark"
    - "vertex feature encoding and hierarchical temporal modeling in a spatial temporal graph convolutional network for action recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-5 (induced by source paper "etri activity3d a large scale rgb d dataset for robots to recognize daily activities of the elderly"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "ensemble one dimensional convolution neural networks for skeleton based action recognition"
    - "neural networks with activation networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "cooperative training of deep aggregation networks for rgb d action recognition"

Connected through hyperedge HE-6 (induced by source paper "semantics guided neural networks for efficient skeleton based human action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "eleatt rnn adding attentiveness to neurons in recurrent neural networks"
    - "deep high resolution representation learning for human pose estimation"
    - "learning deep bilinear transformation for fine grained image representation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "adding attentiveness to the neurons in recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "rsa randomized simulation as augmentation for robust human action recognition"):
  with members:
    - "meta sim learning to generate synthetic datasets"
    - "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"
    - "cycada cycle consistent adversarial domain adaptation"

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"
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "human action recognition and prediction a survey"
    - "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)