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: "two stream rnn cnn for action recognition in 3d videos"
  Abstract: The recognition of actions from video sequences has many applications in health monitoring, assisted living, surveillance, and smart homes. Despite advances in sensing, in particular related to 3D video, the methodologies to process the data are still subject to research. We demonstrate superior results by a system which combines recurrent neural networks with convolutional neural networks in a voting approach. The gated-recurrent-unit-based neural networks are particularly well-suited to distinguish actions based on long-term information from optical tracking data; the 3D-CNNs focus more on detailed, recent information from video data. The resulting features are merged in an SVM which then classifies the movement. In this architecture, our method improves recognition rates of state-of-the-art methods by 14% on standard data sets.

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"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "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"

Connected through hyperedge HE-2 (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"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "etri activity3d a large scale rgb d dataset for robots to recognize daily activities of the elderly"):
  with members:
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "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"
    - "cooperative training of deep aggregation networks for rgb d action recognition"

Connected through hyperedge HE-4 (induced by source paper "action recognition using volumetric motion representations"):
  with members:
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "unsupervised learning of view invariant action representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "cooperative training of deep aggregation networks for rgb d action recognition"
    - "unsupervised learning of long term motion dynamics for videos"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "agent prioritization for autonomous navigation"):
  with members:
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "multi context attention for human pose estimation"
    - "xgboost a scalable tree boosting system"
    - "learning to track at 100 fps with deep regression networks"
    - "active ranking using pairwise comparisons"
    - "learning to order things"

Connected through hyperedge HE-6 (induced by source paper "towards robust direction invariance in character animation"):
  with members:
    - "on the continuity of rotation representations in neural networks"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "on human motion prediction using recurrent neural networks"
    - "structural rnn deep learning on spatio temporal graphs"
    - "recurrent network models for human dynamics"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-7 (induced by source paper "curiosity driven experience prioritization via density estimation"):
  with members:
    - "efficient dialog policy learning via positive memory retention"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "energy based hindsight experience prioritization"
    - "learning goal oriented visual dialog via tempered policy gradient"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "curiosity driven exploration by self supervised prediction"
    - "learning to push by grasping using multiple tasks for effective learning"
    - "vime variational information maximizing exploration"

Connected through hyperedge HE-8 (induced by source paper "duplo a dual view point deep learning architecture for time series classification"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "mrfusion a deep learning architecture to fuse pan and ms imagery for land cover mapping"
    - "multi temporal land cover classification with sequential recurrent encoders"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "efficient attention using a fixed size memory representation"
    - "conditional image generation with pixelcnn decoders"

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)