human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "recurrent batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rethinking batch normalization in transformers")
  Member "drawing and recognizing chinese characters with recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep independently recurrent neural network indrnn")
  Member "convolutional lstm network a machine learning approach for precipitation nowcasting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "sequence to sequence video to text":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modal dense video captioning")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "long term recurrent convolutional networks for visual recognition and description":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)