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: "digital watermarking for deep neural networks"
  Abstract: Although deep neural networks have made tremendous progress in the area of multimedia representation, training neural models requires a large amount of data and time. It is well known that utilizing trained models as initial weights often achieves lower training error than neural networks that are not pre-trained. A fine-tuning step helps to both reduce the computational cost and improve the performance. Therefore, sharing trained models has been very important for the rapid progress of research and development. In addition, trained models could be important assets for the owner(s) who trained them; hence, we regard trained models as intellectual property. In this paper, we propose a digital watermarking technology for ownership authorization of deep neural networks. First, we formulate a new problem: embedding watermarks into deep neural networks. We also define requirements, embedding situations, and attack types on watermarking in deep neural networks. Second, we propose a general framework for embedding a watermark in model parameters, using a parameter regularizer. Our approach does not impair the performance of networks into which a watermark is placed because the watermark is embedded while training the host network. Finally, we perform comprehensive experiments to reveal the potential of watermarking deep neural networks as the basis of this new research effort. We show that our framework can embed a watermark during the training of a deep neural network from scratch, and during fine-tuning and distilling, without impairing its performance. The embedded watermark does not disappear even after fine-tuning or parameter pruning; the watermark remains complete even after 65% of parameters are pruned.

Connected through hyperedge HE-1 (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"
    - "knockoff nets stealing functionality of black box models"

Connected through hyperedge HE-2 (induced by source paper "model watermarking for image processing networks"):
  with members:
    - "stegastamp invisible hyperlinks in physical photographs"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "density aware single image de raining using a multi stream dense network"
    - "hidden hiding data with deep networks"
    - "attention is all you need"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "embedding watermarks into deep neural networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "on the robustness of the backdoor based watermarking in deep neural networks"):
  with members:
    - "robust watermarking of neural network with exponential weighting"
    - "strip a defence against trojan attacks on deep neural networks"
    - "deepstego protecting intellectual property of deep neural networks by steganography"
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "knockoff nets stealing functionality of black box models"
    - "performance comparison of contemporary dnn watermarking techniques"
    - "deepmarks a digital fingerprinting framework for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "ipguard protecting the intellectual property of deep neural networks via fingerprinting the classification boundary"):
  with members:
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "prada protecting against dnn model stealing attacks"
    - "stealing hyperparameters in machine learning"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-5 (induced by source paper "tampernn efficient tampering detection of deployed neural nets"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "have you stolen my model evasion attacks against deep neural network watermarking techniques"
    - "sok security and privacy in machine learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "magnet a two pronged defense against adversarial examples"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-6 (induced by source paper "stealing knowledge from protected deep neural networks using composite unlabeled data"):
  with members:
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "have you stolen my model evasion attacks against deep neural network watermarking techniques"
    - "defending against machine learning model stealing attacks using deceptive perturbations"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "deepapt nation state apt attribution using end to end deep neural networks"
    - "embedding watermarks into deep neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "adversarial frontier stitching for remote neural network watermarking"

Connected through hyperedge HE-7 (induced by source paper "a novel method for identifying the deep neural network model with the serial number"):
  with members:
    - "rethinking deep neural network ownership verification embedding passports to defeat ambiguity attacks"
    - "bringing giant neural networks down to earth with unlabeled data"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "embedding watermarks into deep neural networks"
    - "pruning convolutional neural networks for resource efficient inference"
    - "deep residual learning for image recognition"
    - "net2net accelerating learning via knowledge transfer"

Connected through hyperedge HE-8 (induced by source paper "a first look at deep learning apps on smartphones"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "deepcache principled cache for mobile deep vision"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "securing input data of deep learning inference systems via partitioned enclave execution"
    - "understanding diverse usage patterns from large scale appstore service profiles"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)