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: "sparse representation based open set recognition"
  Abstract: We propose a generalized Sparse Representation- based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification. As most of the discriminative information for open set recognition is hidden in the tail part of the matched and sum of non-matched reconstruction error distributions, we model the tail of those two error distributions using the statistical Extreme Value Theory (EVT). Then we simplify the open set recognition problem into a set of hypothesis testing problems. The confidence scores corresponding to the tail distributions of a novel test sample are then fused to determine its identity. The effectiveness of the proposed method is demonstrated using four publicly available image and object classification datasets and it is shown that this method can perform significantly better than many competitive open set recognition algorithms. Code is public available: this https URL

Connected through hyperedge HE-1 (induced by source paper "conditional gaussian distribution learning for open set recognition"):
  with members:
    - "mnemonics training multi class incremental learning without forgetting"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "doc deep open classification of text documents"
    - "generative openmax for multi class open set classification"
    - "mask r cnn"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-2 (induced by source paper "transfer adaptation learning a decade survey"):
  with members:
    - "optimal projection guided transfer hashing for image retrieval"
    - "representation learning by rotating your faces"
    - "open set domain adaptation for image and action recognition"
    - "deep visual domain adaptation a survey"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "load balanced gans for multi view face image synthesis"
    - "learning domain invariant subspace using domain features and independence maximization"
    - "importance weighted adversarial nets for partial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "deep cnn based multi task learning for open set recognition"):
  with members:
    - "learning deep features for one class classification"
    - "open category detection with pac guarantees"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "and autoregressive novelty detectors"
    - "learning from noisy web data with category level supervision"
    - "classification reconstruction learning for open set recognition"
    - "the extreme value machine"

Connected through hyperedge HE-4 (induced by source paper "c2ae class conditioned auto encoder for open set recognition"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "learning deep features for one class classification"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "dafe fd density aware feature enrichment for face detection"
    - "reducing network agnostophobia"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-5 (induced by source paper "learning deep features for one class classification"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "latent space autoregression for novelty detection"
    - "and autoregressive novelty detectors"
    - "anomaly detection using one class neural networks"
    - "active user authentication for smartphones a challenge data set and benchmark results"

Connected through hyperedge HE-6 (induced by source paper "improved adversarial robustness by reducing open space risk via tent activations"):
  with members:
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "evidential deep learning to quantify classification uncertainty"
    - "efficient formal safety analysis of neural networks"
    - "self normalizing neural networks"
    - "extremeweather a large scale climate dataset for semi supervised detection localization and understanding of extreme weather events"
    - "noisy activation functions"
    - "wide residual networks"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-7 (induced by source paper "transformation of dense and sparse text representations"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "consistent dialogue generation with self supervised feature learning"
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "investigating capsule networks with dynamic routing for text classification"
    - "attention is all you need"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "spine sparse interpretable neural embeddings"
    - "multiresolution recurrent neural networks an application to dialogue response generation"

Connected through hyperedge HE-8 (induced by source paper "cbhe corner based building height estimation for complex street scene images"):
  with members:
    - "triplet center loss for multi view 3d object retrieval"
    - "margin sample mining loss a deep learning based method for person re identification"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "combining maps and street level images for building height and facade estimation"
    - "towards open set deep networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "metric localization using google street view"
    - "caffe convolutional architecture for fast feature embedding"

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)