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: "normal integration a survey"
  Abstract: The need for efficient normal integration methods is driven by several computer vision tasks such as shape-from-shading, photometric stereo, deflectometry, etc. In the first part of this survey, we select the most important properties that one may expect from a normal integration method, based on a thorough study of two pioneering works by Horn and Brooks [28] and by Frankot and Chellappa [19]. Apart from accuracy, an integration method should at least be fast and robust to a noisy normal field. In addition, it should be able to handle several types of boundary condition, including the case of a free boundary, and a reconstruction domain of any shape i.e., which is not necessarily rectangular. It is also much appreciated that a minimum number of parameters have to be tuned, or even no parameter at all. Finally, it should preserve the depth discontinuities. In the second part of this survey, we review most of the existing methods in view of this analysis, and conclude that none of them satisfies all of the required properties. This work is complemented by a companion paper entitled Variational Methods for Normal Integration, in which we focus on the problem of normal integration in the presence of depth discontinuities, a problem which occurs as soon as there are occlusions.

Connected through hyperedge HE-1 (induced by source paper "inferring super resolution depth from a moving light source enhanced rgb d sensor a variational approach"):
  with members:
    - "on the well posedness of uncalibrated photometric stereo under general lighting"
    - "photometric depth super resolution"
    - "a differential volumetric approach to multi view photometric stereo"
    - "detailed surface geometry and albedo recovery from rgb d video under natural illumination"
    - "led based photometric stereo modeling calibration and numerical solution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep depth super resolution learning depth super resolution using deep convolutional neural network"
    - "a deep primal dual network for guided depth super resolution"

Connected through hyperedge HE-2 (induced by source paper "variational uncalibrated photometric stereo under general lighting"):
  with members:
    - "deep photometric stereo on a sunny day"
    - "photometric depth super resolution"
    - "depth super resolution meets uncalibrated photometric stereo"

Connected through hyperedge HE-3 (induced by source paper "photometric depth super resolution"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "a variational approach to shape from shading under natural illumination"
    - "learning to predict indoor illumination from a single image"
    - "led based photometric stereo modeling calibration and numerical solution"
    - "variational methods for normal integration"
    - "neural face editing with intrinsic image disentangling"
    - "learning non lambertian object intrinsics across shapenet categories"

Connected through hyperedge HE-4 (induced by source paper "variational methods for normal integration"):
  with members:
    - "fast and accurate surface normal integration on non rectangular domains"
    - "a nearly mlogn time solver for sdd linear systems"

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)