human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "var cnn and dynaflow improved attacks and defenses for website fingerprinting"):
  Abstract: In recent years, there have been many works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user is visiting. However, most of these works rely on manually extracted features, and thus are fragile: a small change in the protocol or a simple defense often renders these attacks useless. In this work, we leverage deep learning techniques to create a more robust attack that does not require any manually extracted features. Specifically, we propose Var-CNN, an attack that uses model variations on convolutional neural networks with both the packet sequence and packet timing data. In open-world settings, Var-CNN attains higher true positive rate and lower false positive rate than any prior work at 90.9% and 0.3%, respectively. Moreover, these improvements are observed even with low amounts of training data, where deep learning techniques often suffer. #R##N#Given the severity of our attacks, we also introduce a new countermeasure, DynaFlow, based on dynamically adjusting flows to protect against website fingerprinting attacks. DynaFlow provides a similar level of security as current state-of-the-art and defeats all attacks, including our own, while being over 40% more efficient than existing defenses. Moreover, unlike many prior defenses, DynaFlow can protect dynamically generated websites as well.

Includes members:
  Member "accurate large minibatch sgd training imagenet in 1 hour":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "decoupled neural interfaces using synthetic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sideways depth parallel training of video models")
  Member "tensorflow large scale machine learning on heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "multi scale context aggregation by dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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.CR(Cryptography and Security)