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: "a novel database of children s spontaneous facial expressions liris cse"
  Abstract: Abstract   Computing environment is moving towards human-centered designs instead of computer centered designs and human's tend to communicate wealth of information through affective states or expressions. Traditional Human Computer Interaction (HCI) based systems ignores bulk of information communicated through those affective states and just caters for user's intentional input. Generally, for evaluating and benchmarking different facial expression analysis algorithms, standardized databases are needed to enable a meaningful comparison. In the absence of comparative tests on such standardized databases it is difficult to find relative strengths and weaknesses of different facial expression recognition algorithms. In this article we present a novel video database for Children's Spontaneous facial Expressions (LIRIS-CSE). Proposed video database contains six basic spontaneous facial expressions shown by 12 ethnically diverse children between the ages of 6 and 12 years with mean age of 7.3 years. To the best of our knowledge, this database is first of its kind as it records and shows spontaneous facial expressions of children. Previously there were few database of children expressions and all of them show posed or exaggerated expressions which are different from spontaneous or natural expressions. Thus, this database will be a milestone for human behavior researchers. This database will be a excellent resource for vision community for benchmarking and comparing results. In this article, we have also proposed framework for automatic expression recognition based on Convolutional Neural Network (CNN) architecture with transfer learning approach. Proposed architecture achieved average classification accuracy of 75% on our proposed database i.e. LIRIS-CSE.

Connected through hyperedge HE-1 (induced by source paper "handwritten optical character recognition ocr a comprehensive systematic literature review slr"):
  with members:
    - "a novel framework for automatic detection of autism a study on corpus callosum and intracranial brain volume"
    - "scene text detection and recognition the deep learning era"
    - "deep residual learning for image recognition"
    - "draw a recurrent neural network for image generation"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "machine recognition of hand written characters using neural networks"
    - "deep big simple neural nets for handwritten digit recognition"

Connected through hyperedge HE-2 (induced by source paper "secondary use of electronic health record opportunities and challenges"):
  with members:
    - "can autism be catered with artificial intelligence assisted intervention technology a comprehensive survey"
    - "are we there yet understanding the challenges faced in complying with the general data protection regulation gdpr"
    - "privacy issues and data protection in big data a case study analysis under gdpr"

Connected through hyperedge HE-3 (induced by source paper "a novel machine learning based framework for detection of autism spectrum disorder asd"):
  with members:
    - "can autism be catered with artificial intelligence assisted intervention technology a comprehensive survey"
    - "functional connectivity patterns of autism spectrum disorder identified by deep feature learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "an extensive review on spectral imaging in biometric systems challenges and advancements"):
  with members:
    - "what do we understand about convolutional networks"
    - "deep cross polarimetric thermal to visible face recognition"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "deep perceptual mapping for cross modal face recognition"
    - "image level classification in hyperspectral images using feature descriptors with application to face recognition"
    - "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding"
    - "deep perceptual mapping for thermal to visible face recognition"
    - "understanding deep image representations by inverting them"

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)