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: "open set adversarial examples"
  Abstract: Adversarial examples in recent works target at closed set recognition systems, in which the training and testing classes are identical. In real-world scenarios, however, the testing classes may have limited, if any, overlap with the training classes, a problem named open set recognition. To our knowledge, the community does not have a specific design of adversarial examples targeting at this practical setting. Arguably, the new setting compromises traditional closed set attack methods in two aspects. First, closed set attack methods are based on classification and target at classification as well, but the open set problem suggests a different task, \emph{i.e.,} retrieval. It is undesirable that the generation mechanism of closed set recognition is different from the aim of open set recognition. Second, given that the query image is usually of an unseen class, predicting its category from the training classes is not reasonable, which leads to an inferior adversarial gradient. In this work, we view open set recognition as a retrieval task and propose a new approach, Opposite-Direction Feature Attack (ODFA), to generate adversarial examples / queries. When using an attacked example as query, we aim that the true matches be ranked as low as possible. In addressing the two limitations of closed set attack methods, ODFA directly works on the features for retrieval. The idea is to push away the feature of the adversarial query in the opposite direction of the original feature. Albeit simple, ODFA leads to a larger drop in Recall@K and mAP than the close-set attack methods on two open set recognition datasets, \emph{i.e.,} Market-1501 and CUB-200-2011. We also demonstrate that the attack performance of ODFA is not evidently superior to the state-of-the-art methods under closed set recognition (Cifar-10), suggesting its specificity for open set problems.

Connected through hyperedge HE-1 (induced by source paper "targeted mismatch adversarial attack query with a flower to retrieve the tower"):
  with members:
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "universal perturbation attack against image retrieval"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "siamese network of deep fisher vector descriptors for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-2 (induced by source paper "universal adversarial perturbations against person re identification"):
  with members:
    - "unsupervised adversarial attacks on deep feature based retrieval with gan"
    - "joint discriminative and generative learning for person re identification"
    - "learning with average precision training image retrieval with a listwise loss"
    - "metric attack and defense for person re identification"
    - "adversarial metric attack for person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "re identification with consistent attentive siamese networks"

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"

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)