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: "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets"
  Abstract: Network traffic classification, which has numerous applications from security to billing and network provisioning, has become a cornerstone of today's computer networks. Previous studies have developed traffic classification techniques using classical machine learning algorithms and deep learning methods when large quantities of labeled data are available. However, capturing large labeled datasets is a cumbersome and time-consuming process. In this paper, we propose a semi-supervised approach that obviates the need for large labeled datasets. We first pre-train a model on a large unlabeled dataset where the input is the time series features of a few sampled packets. Then the learned weights are transferred to a new model that is re-trained on a small labeled dataset. We show that our semi-supervised approach achieves almost the same accuracy as a fully-supervised method with a large labeled dataset, though we use only 20 samples per class. In tests based on a dataset generated from the more challenging QUIC protocol, our approach yields 98% accuracy. To show its efficacy, we also test our approach on two public datasets. Moreover, we study three different sampling techniques and demonstrate that sampling packets from an arbitrary portion of a flow is sufficient for classification.

Connected through hyperedge HE-1 (induced by source paper "adversarial network traffic toward evaluating the robustness of deep learning based network traffic classification"):
  with members:
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "sok security and privacy in machine learning"
    - "automated website fingerprinting through deep learning"
    - "robust smartphone app identification via encrypted network traffic analysis"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-2 (induced by source paper "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"):
  with members:
    - "security of deep learning methodologies challenges and opportunities"
    - "model reuse attacks on deep learning systems"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "deep learning for encrypted traffic classification an overview"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "are you tampering with my data"
    - "the extreme value machine"

Connected through hyperedge HE-3 (induced by source paper "large scale mobile app identification using deep learning"):
  with members:
    - "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"
    - "multitask learning for network traffic classification"
    - "overcoming catastrophic forgetting problem by weight consolidation and long term memory"
    - "deep learning for encrypted traffic classification an overview"
    - "the dark side channel of mobile devices a survey on network traffic analysis"
    - "robust smartphone app identification via encrypted network traffic analysis"
    - "can t you hear me knocking identification of user actions on android apps via traffic analysis"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "multitask learning for network traffic classification"):
  with members:
    - "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"
    - "overcoming catastrophic forgetting problem by weight consolidation and long term memory"
    - "deep learning for encrypted traffic classification an overview"

Connected through hyperedge HE-5 (induced by source paper "deep learning for encrypted traffic classification an overview"):
  with members:
    - "active one shot learning"
    - "deep packet a novel approach for encrypted traffic classification using deep learning"
    - "robust smartphone app identification via encrypted network traffic analysis"

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.NI(Networking and Internet Architecture)