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: "kinematic layout aware random forests for depth based action recognition"
  Abstract: In this paper, we tackle the problem of 24 hours-monitoring patient actions in a ward such as "stretching an arm out of the bed", "falling out of the bed", where temporal movements are subtle or significant. In the concerned scenarios, the relations between scene layouts and body kinematics (skeletons) become important cues to recognize actions; however they are hard to be secured at a testing stage. To address this problem, we propose a kinematic-layout-aware random forest which takes into account the kinematic-layout (\ie layout and skeletons), to maximize the discriminative power of depth image appearance. We integrate the kinematic-layout in the split criteria of random forests to guide the learning process by 1) determining the switch to either the depth appearance or the kinematic-layout information, and 2) implicitly closing the gap between two distributions obtained by the kinematic-layout and the appearance, when the kinematic-layout appears useful. The kinematic-layout information is not required for the test data, thus called "privileged information prior". The proposed method has also been testified in cross-view settings, by the use of view-invariant features and enforcing the consistency among synthetic-view data. Experimental evaluations on our new dataset PATIENT, CAD-60 and UWA3D (multiview) demonstrate that our method outperforms various state-of-the-arts.

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 "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "analysis of hand segmentation in the wild"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"

Connected through hyperedge HE-3 (induced by source paper "exploiting deep residual networks for human action recognition from skeletal data"):
  with members:
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "unsupervised learning of long term motion dynamics for videos"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "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"
    - "action recognition based on joint trajectory maps using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "category level 6d object pose recovery in depth images"):
  with members:
    - "bop benchmark for 6d object pose estimation"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "iterative hough forest with histogram of control points for 6 dof object registration from depth images"

Connected through hyperedge HE-5 (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"
    - "action recognition with dynamic image networks"
    - "very deep convolutional networks for large scale image recognition"
    - "hopc histogram of oriented principal components of 3d pointclouds 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)