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: "reflectance adaptive filtering improves intrinsic image estimation"
  Abstract: Separating an image into reflectance and shading layers poses a challenge for learning approaches because no large corpus of precise and realistic ground truth decompositions exists. The Intrinsic Images in the Wild~(IIW) dataset provides a sparse set of relative human reflectance judgments, which serves as a standard benchmark for intrinsic images. A number of methods use IIW to learn statistical dependencies between the images and their reflectance layer. Although learning plays an important role for high performance, we show that a standard signal processing technique achieves performance on par with current state-of-the-art. We propose a loss function for CNN learning of dense reflectance predictions. Our results show a simple pixel-wise decision, without any context or prior knowledge, is sufficient to provide a strong baseline on IIW. This sets a competitive baseline which only two other approaches surpass. We then develop a joint bilateral filtering method that implements strong prior knowledge about reflectance constancy. This filtering operation can be applied to any intrinsic image algorithm and we improve several previous results achieving a new state-of-the-art on IIW. Our findings suggest that the effect of learning-based approaches may have been over-estimated so far. Explicit prior knowledge is still at least as important to obtain high performance in intrinsic image decompositions.

Connected through hyperedge HE-1 (induced by source paper "neural inverse rendering of an indoor scene from a single image"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "learning intrinsic image decomposition from watching the world"
    - "inverserendernet learning single image inverse rendering"
    - "inverse transport networks"
    - "materials for masses svbrdf acquisition with a single mobile phone image"

Connected through hyperedge HE-2 (induced by source paper "semantic hierarchical priors for intrinsic image decomposition"):
  with members:
    - "deep hybrid real and synthetic training for intrinsic decomposition"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning intrinsic image decomposition from watching the world"
    - "shading annotations in the wild"
    - "unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "how transferable are features in deep neural networks"
    - "depth transfer depth extraction from video using non parametric sampling"

Connected through hyperedge HE-3 (induced by source paper "structural decompositions for end to end relighting"):
  with members:
    - "neural inverse rendering of an indoor scene from a single image"
    - "synthetic depth of field with a single camera mobile phone"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning to predict indoor illumination from a single image"
    - "neural face editing with intrinsic image disentangling"
    - "shading annotations in the wild"
    - "deep outdoor illumination estimation"

Connected through hyperedge HE-4 (induced by source paper "inverserendernet learning single image inverse rendering"):
  with members:
    - "deep hybrid real and synthetic training for intrinsic decomposition"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "learning intrinsic image decomposition from watching the world"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "megadepth learning single view depth prediction from internet photos"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-5 (induced by source paper "cgintrinsics better intrinsic image decomposition through physically based rendering"):
  with members:
    - "learning intrinsic image decomposition from watching the world"
    - "neural face editing with intrinsic image disentangling"
    - "self supervised intrinsic image decomposition"
    - "playing for benchmarks"
    - "shading annotations in the wild"
    - "semantic scene completion from a single depth image"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "learning non lambertian object intrinsics across shapenet categories"

Connected through hyperedge HE-6 (induced by source paper "detailed human avatars from monocular video"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "end to end recovery of human shape and pose"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"

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)