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: "characteristics of visual categorization of long concatenated and object directed human actions by a multiple spatio temporal scales recurrent neural network model"
  Abstract: The current paper proposes a novel dynamic neural network model, multiple spatio-temporal scales recurrent neural network (MSTRNN) used for categorization of complex human action pattern in video image. The MSTRNN has been developed by newly introducing recurrent connectivity to a prior-proposed model, multiple spatio-temporal scales neural network (MSTNN) [1] such that the model can learn to extract latent spatio-temporal structures more effectively by developing adequate recurrent contextual dynamics. The MSTRNN was evaluated by conducting a set of simulation experiments on learning to categorize human action visual patterns. The first experiment on categorizing a set of long-concatenated human movement patterns showed that MSTRNN outperforms MSTNN in the capability of learning to extract long-ranged correlation in video image. The second experiment on categorizing a set of object-directed actions showed that the MSTRNN can learn to extract structural relationship between actions and directed-objects. Our analysis on the characteristics of miscategorization in both cases of object-directed action and pantomime actions indicated that the model network developed the categorical memories by organizing relational structure among them. Development of such relational structure is considered to be beneficial for gaining generalization in categorization.

Connected through hyperedge HE-1 (induced by source paper "afa prednet the action modulation within predictive coding"):
  with members:
    - "toward abstraction from multi modal data empirical studies on multiple time scale recurrent models"
    - "sensorimotor input as a language generalisation tool a neurorobotics model for generation and generalisation of noun verb combinations with sensorimotor inputs"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "recognition of visually perceived compositional human actions by multiple spatio temporal scales recurrent neural networks"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-2 (induced by source paper "predictive coding for dynamic visual processing development of functional hierarchy in a multiple spatio temporal scales rnn model"):
  with members:
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "unsupervised learning of video representations using lstms"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "unsupervised learning of visual structure using predictive generative networks"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)