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: "tri subject kinship verification understanding the core of a family"
  Abstract: One major challenge in computer vision is to go beyond the modeling of individual objects and to investigate the bi- (one-versus-one) or tri- (one-versus-two) relationship among multiple visual entities, answering such questions as whether a child in a photo belongs to given parents. The child-parents relationship plays a core role in a family and understanding such kin relationship would have fundamental impact on the behavior of an artificial intelligent agent working in the human world. In this work, we tackle the problem of one-versus-two (tri-subject) kinship verification and our contributions are three folds: 1) a novel relative symmetric bilinear model (RSBM) introduced to model the similarity between the child and the parents, by incorporating the prior knowledge that a child may resemble a particular parent more than the other; 2) a spatially voted method for feature selection, which jointly selects the most discriminative features for the child-parents pair, while taking local spatial information into account; 3) a large scale tri-subject kinship database characterized by over 1,000 child-parents families. Extensive experiments on KinFaceW, Family101 and our newly released kinship database show that the proposed method outperforms several previous state of the art methods, while could also be used to significantly boost the performance of one-versus-one kinship verification when the information about both parents are available.

Connected through hyperedge HE-1 (induced by source paper "controllable descendant face synthesis"):
  with members:
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "kinshipgan synthesizing of kinship faces from family photos by regularizing a deep face network"
    - "unsupervised holistic image generation from key local patches"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-2 (induced by source paper "recognizing families in the wild rfiw the 4th edition"):
  with members:
    - "what will your child look like dna net age and gender aware kin face synthesizer"
    - "arcface additive angular margin loss for deep face recognition"
    - "kinshipgan synthesizing of kinship faces from family photos by regularizing a deep face network"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "families in the wild fiw large scale kinship image database and benchmarks"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "audio visual kinship verification"):
  with members:
    - "supervised mixed norm autoencoder for kinship verification in unconstrained videos"
    - "from same photo cheating on visual kinship challenges"
    - "towards good practices for multi modal fusion in large scale video classification"
    - "voxceleb2 deep speaker recognition"
    - "multimodal machine learning a survey and taxonomy"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "supervised mixed norm autoencoder for kinship verification in unconstrained videos"):
  with members:
    - "hierarchical representation learning for kinship verification"
    - "families in the wild fiw large scale kinship image database and benchmarks"

Connected through hyperedge HE-5 (induced by source paper "from same photo cheating on visual kinship challenges"):
  with members:
    - "supervised mixed norm autoencoder for kinship verification in unconstrained videos"
    - "hierarchical representation learning for kinship verification"
    - "families in the wild fiw large scale kinship image database and benchmarks"
    - "unsupervised visual representation learning by context prediction"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-6 (induced by source paper "families in the wild fiw large scale kinship image database and benchmarks"):
  with members:
    - "face search at scale 80 million gallery"
    - "facenet a unified embedding for face recognition and clustering"
    - "caffe convolutional architecture for fast feature embedding"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "group membership prediction"):
  with members:
    - "zero shot learning via semantic similarity embedding"
    - "prism person re identification via structured matching"
    - "a novel visual word co occurrence model for person re identification"
    - "a survey on multi view learning"

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)