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: "knockoff nets stealing functionality of black box models"
  Abstract: Machine Learning (ML) models are increasingly deployed in the wild to perform a wide range of tasks. In this work, we ask to what extent can an adversary steal functionality of such "victim" models based solely on blackbox interactions: image in, predictions out. In contrast to prior work, we present an adversary lacking knowledge of train/test data used by the model, its internals, and semantics over model outputs. We formulate model functionality stealing as a two-step approach: (i) querying a set of input images to the blackbox model to obtain predictions; and (ii) training a "knockoff" with queried image-prediction pairs. We make multiple remarkable observations: (a) querying random images from a different distribution than that of the blackbox training data results in a well-performing knockoff; (b) this is possible even when the knockoff is represented using a different architecture; and (c) our reinforcement learning approach additionally improves query sample efficiency in certain settings and provides performance gains. We validate model functionality stealing on a range of datasets and tasks, as well as on a popular image analysis API where we create a reasonable knockoff for as little as $30.

Connected through hyperedge HE-1 (induced by source paper "dynamic backdoor attacks against machine learning models"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "strip a defence against trojan attacks on deep neural networks"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"

Connected through hyperedge HE-2 (induced by source paper "entangled watermarks as a defense against model extraction"):
  with members:
    - "similarity of neural network representations revisited"
    - "high fidelity extraction of neural network models"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "energy and policy considerations for deep learning in nlp"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"

Connected through hyperedge HE-3 (induced by source paper "model extraction attacks against recurrent neural networks"):
  with members:
    - "high fidelity extraction of neural network models"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "dawn dynamic adversarial watermarking of neural networks"
    - "extraction of complex dnn models real threat or boogeyman"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "prada protecting against dnn model stealing attacks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stealing hyperparameters in machine learning"

Connected through hyperedge HE-4 (induced by source paper "prediction poisoning towards defenses against dnn model stealing attacks"):
  with members:
    - "high fidelity extraction of neural network models"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "exploring connections between active learning and model extraction"
    - "stealing hyperparameters in machine learning"
    - "model extraction warning in mlaas paradigm"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-5 (induced by source paper "stealing black box functionality using the deep neural tree architecture"):
  with members:
    - "neural network gradient based learning of black box function interfaces"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "neural arithmetic logic units"
    - "hyperbolic neural networks"
    - "query efficient black box attack by active learning"
    - "adaptive neural trees"
    - "stealing hyperparameters in machine learning"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-6 (induced by source paper "mind your weight s a large scale study on insufficient machine learning model protection in mobile apps"):
  with members:
    - "high fidelity extraction of neural network models"
    - "on device neural net inference with mobile gpus"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "a first look at deep learning apps on smartphones"
    - "privado practical and secure dnn inference"
    - "stealing hyperparameters in machine learning"

Connected through hyperedge HE-7 (induced by source paper "survey of attacks and defenses on edge deployed neural networks"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "bit flip attack crushing neural network with progressive bit search"
    - "xonn xnor based oblivious deep neural network inference"
    - "deeplaser practical fault attack on deep neural networks"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"

Connected through hyperedge HE-8 (induced by source paper "deep neural network fingerprinting by conferrable adversarial examples"):
  with members:
    - "structure matters towards generating transferable adversarial images"
    - "high fidelity extraction of neural network models"
    - "on the robustness of the backdoor based watermarking in deep neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "ipguard protecting the intellectual property of deep neural networks via fingerprinting the classification boundary"
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "dawn dynamic adversarial watermarking of neural networks"
    - "on evaluating adversarial robustness"

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)