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: "comparator networks"
  Abstract: The objective of this work is set-based verification, e.g. to decide if two sets of images of a face are of the same person or not. The traditional approach to this problem is to learn to generate a feature vector per image, aggregate them into one vector to represent the set, and then compute the cosine similarity between sets. Instead, we design a neural network architecture that can directly learn set-wise verification. Our contributions are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of sets (each may contain a variable number of images) as inputs, and compute a similarity between the pair--this involves attending to multiple discriminative local regions (landmarks), and comparing local descriptors between pairs of faces; (ii) To encourage high-quality representations for each set, internal competition is introduced for recalibration based on the landmark score; (iii) Inspired by image retrieval, a novel hard sample mining regime is proposed to control the sampling process, such that the DCN is complementary to the standard image classification models. Evaluations on the IARPA Janus face recognition benchmarks show that the comparator networks outperform the previous state-of-the-art results by a large margin.

Connected through hyperedge HE-1 (induced by source paper "generalizing face representation with unlabeled data"):
  with members:
    - "circle loss a unified perspective of pair similarity optimization"
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "probabilistic face embeddings"
    - "domain generalization by solving jigsaw puzzles"
    - "retinaface single stage dense face localisation in the wild"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-2 (induced by source paper "distribution distillation loss generic approach for improving face recognition from hard samples"):
  with members:
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"
    - "support vector guided softmax loss for face recognition"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (induced by source paper "fine grained instance level sketch based video retrieval"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "deep self supervised representation learning for free hand sketch"
    - "livesketch query perturbations for guided sketch based visual search"
    - "deep zero shot learning for scene sketch"
    - "learning deep sketch abstraction"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "multi fiber networks for video recognition"
    - "sketchmate deep hashing for million scale human sketch retrieval"

Connected through hyperedge HE-4 (induced by source paper "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"):
  with members:
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge transfer with jacobian matching"
    - "paraphrasing complex network network compression via factor transfer"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "multicolumn networks for face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"

Connected through hyperedge HE-5 (induced by source paper "probabilistic face embeddings"):
  with members:
    - "striking the right balance with uncertainty"
    - "video face recognition component wise feature aggregation network c fan"
    - "cosface large margin cosine loss for deep face recognition"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "towards interpretable face recognition"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-6 (induced by source paper "attention control with metric learning alignment for image set based recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "conservative wasserstein training for pose estimation"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "multicolumn networks for face recognition"
    - "revisiting temporal modeling for video based person reid"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "learning policies for adaptive tracking with deep feature cascades"

Connected through hyperedge HE-7 (induced by source paper "permutation invariant feature restructuring for correlation aware image set based recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "conservative wasserstein training for pose estimation"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "deep sets"
    - "multicolumn networks for face recognition"
    - "interpretable set functions"
    - "nonlocal neural networks nonlocal diffusion and nonlocal modeling"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-8 (induced by source paper "recurrent embedding aggregation network for video face recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "multi prototype networks for unconstrained set based face recognition"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "video face recognition component wise feature aggregation network c fan"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "ghostvlad for set based face recognition"
    - "multicolumn networks for face recognition"

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)