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 "controlling neural networks via energy dissipation"):
  Abstract: The last decade has shown a tremendous success in solving various computer vision problems with the help of deep learning techniques. Lately, many works have demonstrated that learning-based approaches with suitable network architectures even exhibit superior performance for the solution of (ill-posed) image reconstruction problems such as deblurring, super-resolution, or medical image reconstruction. The drawback of purely learning-based methods, however, is that they cannot provide provable guarantees for the trained network to follow a given data formation process during inference. In this work we propose energy dissipating networks that iteratively compute a descent direction with respect to a given cost function or energy at the currently estimated reconstruction. Therefore, an adaptive step size rule such as a line-search, along with a suitable number of iterations can guarantee the reconstruction to follow a given data formation model encoded in the energy to arbitrary precision, and hence control the model's behavior even during test time. We prove that under standard assumptions, descent using the direction predicted by the network converges (linearly) to the global minimum of the energy. We illustrate the effectiveness of the proposed approach in experiments on single image super resolution and computed tomography (CT) reconstruction, and further illustrate extensions to convex feasibility problems.

Includes members:
  Member "linear inequality constraints for neural network activations":
    also connected through other hyperedges:
      (none)
  Member "learning converged propagations with deep prior ensemble for image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep plug and play super resolution for arbitrary blur kernels")
  Member "homogeneous linear inequality constraints for neural network activations":
    also connected through other hyperedges:
      (none)
  Member "deep decoder concise image representations from untrained non convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "image recognition from raw labels collected without annotators")
  Member "scale recurrent network for deep image deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning when and where to zoom with deep reinforcement learning")
  Member "deep image prior":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semantic pyramid for image generation")
  Member "recurrent relational networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")

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.LG(Machine Learning)