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: "gaussian material synthesis"
  Abstract: This work presents a learning-based system for rapid mass-scale material synthesis and visualization.

Connected through hyperedge HE-1 (induced by source paper "a similarity measure for material appearance"):
  with members:
    - "learning icons appearance similarity"
    - "on the convergence of adam and beyond"
    - "brdf estimation of complex materials with nested learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "recognizing material properties from images"
    - "revisiting temporal modeling for video based person reid"
    - "a lightweight approach for on the fly reflectance estimation"
    - "an intuitive control space for material appearance"

Connected through hyperedge HE-2 (induced by source paper "photorealistic material editing through direct image manipulation"):
  with members:
    - "deepred deep image prior powered by red"
    - "analyzing inverse problems with invertible neural networks"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "single image svbrdf capture with a rendering aware deep network"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "dynamic routing between capsules"
    - "neural optimizer search with reinforcement learning"
    - "an intuitive control space for material appearance"

Connected through hyperedge HE-3 (induced by source paper "a flexible neural renderer for material visualization"):
  with members:
    - "noise2noise learning image restoration without clean data"
    - "single image svbrdf capture with a rendering aware deep network"
    - "perceptual losses for real time style transfer and super resolution"
    - "u net convolutional networks for biomedical image segmentation"
    - "very deep convolutional networks for large scale image recognition"

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.LG(Machine Learning)