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: "exploiting recurrent neural networks and leap motion controller for the recognition of sign language and semaphoric hand gestures"
  Abstract: Hand gesture recognition is still a topic of great interest for the computer vision community. In particular, sign language and semaphoric hand gestures are two foremost areas of interest due to their importance in human–human communication and human–computer interaction, respectively. Any hand gesture can be represented by sets of feature vectors that change over time. Recurrent neural networks (RNNs) are suited to analyze this type of set thanks to their ability to model the long-term contextual information of temporal sequences. In this paper, an RNN is trained by using as features the angles formed by the finger bones of the human hands. The selected features, acquired by a leap motion controller sensor, are chosen because the majority of human hand gestures produce joint movements that generate truly characteristic corners. The proposed method, including the effectiveness of the selected angles, was initially tested by creating a very challenging dataset composed by a large number of gestures defined by the American sign language. On the latter, an accuracy of over 96% was achieved. Afterwards, by using the Shape Retrieval Contest (SHREC) dataset, a wide collection of semaphoric hand gestures, the method was also proven to outperform in accuracy competing approaches of the current literature.

Connected through hyperedge HE-1 (induced by source paper "deep temporal analysis for non acted body affect recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "lattice long short term memory for human action recognition"
    - "facial expression recognition using visual saliency and deep learning"
    - "neural architecture search with reinforcement learning"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "deepgru deep gesture recognition utility"):
  with members:
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "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)