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: "a framework for the extraction of deep neural networks by leveraging public data"
  Abstract: Machine learning models trained on confidential datasets are increasingly being deployed for profit. Machine Learning as a Service (MLaaS) has made such models easily accessible to end-users. Prior work has developed model extraction attacks, in which an adversary extracts an approximation of MLaaS models by making black-box queries to it. However, none of these works is able to satisfy all the three essential criteria for practical model extraction: (1) the ability to work on deep learning models, (2) the non-requirement of domain knowledge and (3) the ability to work with a limited query budget. We design a model extraction framework that makes use of active learning and large public datasets to satisfy them. We demonstrate that it is possible to use this framework to steal deep classifiers trained on a variety of datasets from image and text domains. By querying a model via black-box access for its top prediction, our framework improves performance on an average over a uniform noise baseline by 4.70x for image tasks and 2.11x for text tasks respectively, while using only 30% (30,000 samples) of the public dataset at its disposal.

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"
    - "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"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"

Connected through hyperedge HE-2 (induced by source paper "model extraction attacks against recurrent neural networks"):
  with members:
    - "high fidelity extraction of neural network models"
    - "dawn dynamic adversarial watermarking of neural networks"
    - "extraction of complex dnn models real threat or boogeyman"
    - "knockoff nets stealing functionality of black box models"
    - "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-3 (induced by source paper "prediction poisoning towards defenses against dnn model stealing attacks"):
  with members:
    - "high fidelity extraction of neural network models"
    - "knockoff nets stealing functionality of black box models"
    - "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-4 (induced by source paper "thieves on sesame street model extraction of bert based apis"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "high fidelity extraction of neural network models"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "learning and evaluating general linguistic intelligence"
    - "membership inference attacks on sequence to sequence models"
    - "prediction poisoning utility constrained defenses against model stealing attacks"
    - "dawn dynamic adversarial watermarking of neural networks"

Connected through hyperedge HE-5 (induced by source paper "high accuracy and high fidelity extraction of neural networks"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "overlearning reveals sensitive attributes"
    - "rethinking deep active learning using unlabeled data at model training"
    - "combining mixmatch and active learning for better accuracy with fewer labels"
    - "on the learnability of deep random networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-6 (induced by source paper "high fidelity extraction of neural network models"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "overlearning reveals sensitive attributes"
    - "on the learnability of deep random networks"
    - "mixmatch a holistic approach to semi supervised learning"
    - "energy and policy considerations for deep learning in nlp"
    - "knockoff nets stealing functionality of black box models"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"

Connected through hyperedge HE-7 (induced by source paper "quantifying hyper parameter leakage in machine learning"):
  with members:
    - "prediction poisoning towards defenses against dnn model stealing attacks"
    - "a tutorial on learning with bayesian networks"
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "f bleau fast black box leakage estimation"
    - "knockoff nets stealing functionality of black box models"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "exploring connections between active learning and model extraction"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"

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.LG(Machine Learning)