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 "similarity dt kernel similarity embedding for dynamic texture synthesis"):
  Abstract: Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess high similarity correlation. However, there are no DT synthesis methods to consider the similarity prior for representing DT instead, which can explicitly capture the homogeneous and heterogeneous correlation between different frames of DT. In this paper, we propose a novel DT synthesis method (named Similarity-DT), which embeds the similarity prior into the representation of DT. Specifically, we first raise two hypotheses: the content of texture video frames varies over time-to-time, while the more closed frames should be more similar; the transition between frame-to-frame could be modeled as a linear or nonlinear function to capture the similarity correlation. Then, our proposed Similarity-DT integrates kernel learning and extreme learning machine (ELM) into a powerful unified synthesis model to learn kernel similarity embedding to represent the spatial-temporal transition among frame-to-frame of DTs. Extensive experiments on DT videos collected from internet and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex, demonstrate that the learned kernel similarity embedding effectively exhibits the discriminative representation for DTs. Hence our method is capable of preserving long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. We also show that our method effectively generates realistic DT videos with fast speed and low computation, compared with the state-of-the-art approaches.

Includes members:
  Member "joint discriminative and generative learning for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "towards optimal discrete online hashing with balanced similarity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ssah semi supervised adversarial deep hashing with self paced hard sample generation")
  Member "semi supervised feature learning for improving writer identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fragnet writer identification using deep fragment networks")
  Member "learning energy based spatial temporal generative convnets for dynamic patterns":
    also connected through other hyperedges:
      (none)
  Member "in defense of the triplet loss for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "two stream convolutional networks for dynamic texture synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligent home 3d automatic 3d house design from linguistic descriptions only")
  Member "photographic image synthesis with cascaded refinement networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "temporal generative adversarial nets with singular value clipping":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")

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)