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: "face synthesis fasy system for generation of a face image from human description"
  Abstract: * AICTE Emeritus Fellow Abstract-This paper aims at generating a new face based on the human like description using a new concept. The FASY (FAce SYnthesis) System is a Face Database Retrieval and new Face generation System that is under development. One of its main features is the generation of the requested face when it is not found in the existing database, which allows a continuous growing of the database also. Index Terms- Adjusting Intensities, Assembling the Components, Face Generation, Databases I. INTRODUCTION Face is most important visual identity of a person and while meeting an unknown person, it is the face that attracts our attention most. We often describe a person in terms of the characteristic features of important face components like eyes, eyebrows, nose, lips together with the overall shape of the face. The criminal Investigation agencies employ artists to sketch human faces from the description given by a witness about criminal's appearance which is then searched in the face database of the known criminals for recognition. Since early 1990's, Face Recognition Technology (FRT) becomes an active research area and there are a lot of works on facial recognition and facial feature extraction (1)(2)(3)(4)(5)(6). In 1994, Jian Kang Wu and Arcot Desai Narasimhalu (7) worked on "Identifying Faces Using Multiple Retrievals" where they retrieved the existing faces based on some properties of the different face components. But if the desired face is not in the database, then it is necessary to construct a new face from the user's description and this work is a new one which is one of the objective of the present work. This paper is a part of a main research effort, whose aim is the construction of the FASY-System based on human like description of each components of the face. The FASY System has the following features: (i) face queries using human-like description of the face, (ii) searching the required face in the database and (iii) generation of the requested face when they are not in the database. This paper is focused in this last feature. All the face components are stored in the database and are retrieved according to the user given queries and combined together to form the whole face which is then shown to the user. The Face Generation system works in 3 steps: (i) Searching phase to search the database for each single components of the face according to the user's query. (ii) Assembling phase to place all the components at proper places to generate the full face (iii) Tuning phase to adjust the intensities to cover up the stitching lines between different pair of face components so that the generated face looks natural. A variety of face databases have already been described in literature and here we have worked with the databases found from AT & T Laboratories Cambridge (8). The specifications for the database described in this paper are as follows: • In all blank facial images, the ears should be visible. • Sufficient variety of facial components and their properties should be stored in the database. • The database should contain subject of different ethnicities, sample of both men and women, and sample from different age groups. • Variation due to head tilt, shift, rotation and scaling should be minimized as much as possible.

Connected through hyperedge HE-1 (induced by source paper "recent progress of face image synthesis"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "generative face completion"
    - "random sampling and locality constraint for face sketch"
    - "face aging with conditional generative adversarial networks"
    - "srefi synthesis of realistic example face images"
    - "genegan learning object transfiguration and attribute subspace from unpaired data"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "multi view image generation from a single view"

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)