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: "toward open set face recognition"
  Abstract: Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using closed-set protocols, which assume that all probe images used in evaluation contain identities of subjects that are enrolled in the gallery. Real systems, however, where only a fraction of probe sample identities are enrolled in the gallery, cannot make this closed-set assumption. Instead, they must assume an open set of probe samples and be able to reject/ignore those that correspond to unknown identities. In this paper, we address the widespread misconception that thresholding verification-like scores is a good way to solve the open-set face identification problem, by formulating an open-set face identification protocol and evaluating different strategies for assessing similarity. Our open-set identification protocol is based on the canonical labeled faces in the wild (LFW) dataset. Additionally to the known identities, we introduce the concepts of known unknowns (known, but uninteresting persons) and unknown unknowns (people never seen before) to the biometric community. We compare three algorithms for assessing similarity in a deep feature space under an open-set protocol: thresholded verification-like scores, linear discriminant analysis (LDA) scores, and an extreme value machine (EVM) probabilities. Our findings suggest that thresholding EVM probabilities, which are open-set by design, outperforms thresholding verification-like scores.

Connected through hyperedge HE-1 (induced by source paper "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"):
  with members:
    - "security of deep learning methodologies challenges and opportunities"
    - "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets"
    - "model reuse attacks on deep learning systems"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "deep learning for encrypted traffic classification an overview"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "are you tampering with my data"

Connected through hyperedge HE-2 (induced by source paper "toward metrics for differentiating out of distribution sets"):
  with members:
    - "selectivenet a deep neural network with an integrated reject option"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "deep anomaly detection with outlier exposure"
    - "metric learning for novelty and anomaly detection"
    - "discriminative out of distribution detection for semantic segmentation"
    - "on calibration of modern neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "open category classification by adversarial sample generation"

Connected through hyperedge HE-3 (induced by source paper "recent advances in open set recognition a survey"):
  with members:
    - "open world learning and application to product classification"
    - "large scale long tailed recognition in an open world"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "bayesian semantic instance segmentation in open set world"
    - "learning a neural network based representation for open set recognition"
    - "open category detection with pac guarantees"
    - "open set domain adaptation by backpropagation"
    - "reducing network agnostophobia"

Connected through hyperedge HE-4 (induced by source paper "modeling uncertainty with hedged instance embedding"):
  with members:
    - "uncertainty in the variational information bottleneck"
    - "rethinking feature distribution for loss functions in image classification"
    - "understanding and controlling user linkability in decentralized learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "no fuss distance metric learning using proxies"
    - "on the emergence of invariance and disentangling in deep representations"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deep variational information bottleneck"

Connected through hyperedge HE-5 (induced by source paper "distribution networks for open set learning"):
  with members:
    - "learning a neural network based representation for open set recognition"
    - "semantic autoencoder for zero shot learning"
    - "prototypical networks for few shot learning"
    - "fearnet brain inspired model for incremental learning"
    - "learning a deep embedding model for zero shot learning"
    - "matching networks for one shot learning"
    - "towards open set deep networks"
    - "towards open world recognition"

Connected through hyperedge HE-6 (induced by source paper "learning from millions of 3d scans for large scale 3d face recognition"):
  with members:
    - "dense 3d face correspondence"
    - "end to end 3d face reconstruction with deep neural networks"
    - "level playing field for million scale face recognition"
    - "deep 3d face identification"
    - "do we really need to collect millions of faces for effective face recognition"
    - "3d face reconstruction by learning from synthetic data"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "unconstrained face detection and open set face recognition challenge"):
  with members:
    - "the extreme value machine"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "finding tiny faces"
    - "multi person pose estimation with local joint to person associations"
    - "umdfaces an annotated face dataset for training deep networks"
    - "deep residual learning for image recognition"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "wider face a face detection benchmark"

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)