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: "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
  Abstract: Deep learning is ubiquitous across many areas areas of computer vision. It often requires large scale datasets for training before being fine-tuned on small-to-medium scale problems. Activity, or, in other words, action recognition, is one of many application areas of deep learning. While there exist many Convolutional Neural Network architectures that work with the RGB and optical flow frames, training on the time sequences of 3D body skeleton joints is often performed via recurrent networks such as LSTM. #R##N#In this paper, we propose a new representation which encodes sequences of 3D body skeleton joints in texture-like representations derived from mathematically rigorous kernel methods. Such a representation becomes the first layer in a standard CNN network e.g., ResNet-50, which is then used in the supervised domain adaptation pipeline to transfer information from the source to target dataset. This lets us leverage the available Kinect-based data beyond training on a single dataset and outperform simple fine-tuning on any two datasets combined in a naive manner. More specifically, in this paper we utilize the overlapping classes between datasets. We associate datapoints of the same class via so-called commonality, known from the supervised domain adaptation. We demonstrate state-of-the-art results on three publicly available benchmarks.

Connected through hyperedge HE-1 (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"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "higher order pooling of cnn features via kernel linearization for action recognition"

Connected through hyperedge HE-2 (induced by source paper "a unified deep framework for joint 3d pose estimation and action recognition from a single rgb camera"):
  with members:
    - "skeletal movement to color map a novel representation for 3d action recognition with inception residual networks"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "efficient neural architecture search via parameter sharing"
    - "exploiting deep residual networks for human action recognition from skeletal data"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "self normalizing neural networks"

Connected through hyperedge HE-3 (induced by source paper "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"):
  with members:
    - "loss switching fusion with similarity search for video classification"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "long term feature banks for detailed video understanding"
    - "learning discriminative video representations using adversarial perturbations"
    - "a deeper look at power normalizations"
    - "non linear temporal subspace representations for activity recognition"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (induced by source paper "coltrane convolutional trajectory network for deep map inference"):
  with members:
    - "a comparative review of recent kinect based action recognition algorithms"
    - "road network fusion for incremental map updates"
    - "riobusdata outlier detection in bus routes of rio de janeiro"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "a comparison and evaluation of map construction algorithms using vehicle tracking data"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "map a crowd sensing system for automatic map semantics identification"

Connected through hyperedge HE-5 (induced by source paper "deepgru deep gesture recognition utility"):
  with members:
    - "exploiting recurrent neural networks and leap motion controller for the recognition of sign language and semaphoric hand gestures"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "non linear temporal subspace representations for activity recognition"
    - "sts classification with dual stream cnn"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "excitation backprop for rnns"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture 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)