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: "facsimile fast and accurate scans from an image in less than a second"
  Abstract: Current methods for body shape estimation either lack detail or require many images. They are usually architecturally complex and computationally expensive. We propose FACSIMILE (FAX), a method that estimates a detailed body from a single photo, lowering the bar for creating virtual representations of humans. Our approach is easy to implement and fast to execute, making it easily deployable. FAX uses an image-translation network which recovers geometry at the original resolution of the image. Counterintuitively, the main loss which drives FAX is on per-pixel surface normals instead of per-pixel depth, making it possible to estimate detailed body geometry without any depth supervision. We evaluate our approach both qualitatively and quantitatively, and compare with a state-of-the-art method.

Connected through hyperedge HE-1 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion"):
  with members:
    - "the virtual tailor predicting clothing in 3d as a function of human pose shape and garment style"
    - "tex2shape detailed full human body geometry from a single image"
    - "geometric disentanglement for generative latent shape models"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "360 degree textures of people in clothing from a single image"
    - "what do single view 3d reconstruction networks learn"

Connected through hyperedge HE-2 (induced by source paper "dressing 3d humans using a conditional mesh vae gan"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "meshgan non linear 3d morphable models of faces"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"
    - "moulding humans non parametric 3d human shape estimation from single images"

Connected through hyperedge HE-3 (induced by source paper "learning to dress 3d people in generative clothing"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "learning to train with synthetic humans"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"

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)