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: "vesselness features and the inverse compositional aam for robust face recognition using thermal ir"
  Abstract: Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. We describe a novel method which addresses these challenges. To normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first publication which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances detail in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieved 100% identification, significantly outperforming previous methods.

Connected through hyperedge HE-1 (induced by source paper "synthesising wider field images from narrow field retinal video acquired using a low cost direct ophthalmoscope arclight attached to a smartphone"):
  with members:
    - "illumination invariant face recognition from a single image across extreme pose using a dual dimension aam ensemble in the thermal infrared spectrum"

Connected through hyperedge HE-2 (induced by source paper "a unified framework for thermal face recognition"):
  with members:
    - "illumination invariant face recognition from a single image across extreme pose using a dual dimension aam ensemble in the thermal infrared spectrum"
    - "infrared face recognition a literature review"
    - "minutiae based thermal face recognition using blood perfusion data"

Connected through hyperedge HE-3 (induced by source paper "infrared face recognition a comprehensive review of methodologies and databases"):
  with members:
    - "infrared face recognition a literature review"
    - "minutiae based thermal face recognition using blood perfusion data"
    - "face recognition using 3d facial shape and color map information comparison and combination"

Connected through hyperedge HE-4 (induced by source paper "infrared face recognition a literature review"):
  with members:
    - "illumination invariant face recognition from a single image across extreme pose using a dual dimension aam ensemble in the thermal infrared spectrum"
    - "minutiae based thermal face recognition using blood perfusion data"

Connected through hyperedge HE-5 (induced by source paper "illumination invariant face recognition from a single image across extreme pose using a dual dimension aam ensemble in the thermal infrared spectrum"):
  with members:
    - "infrared face recognition a literature review"

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)