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 general retraining framework for scalable adversarial classification"
  Abstract: Traditional classification algorithms assume that training and test data come from the same or similar distribution. This assumption is violated in adversarial settings, where malicious actors modify instances to evade detection. A number of custom methods have been developed for both adversarial evasion attacks and robust learning. We propose the first systematic and general-purpose retraining framework which can: a) boost robustness of an arbitrary learning algorithm, and b) incorporate a broad class of adversarial models. We show that, under natural conditions, the retraining framework minimizes an upper bound on optimal adversarial risk, and show how to extend this result to account for approximations of evasion attacks. We also offer a very general adversarial evasion model and algorithmic framework based on coordinate greedy local search. Extensive experimental evaluation demonstrates that our retraining methods are nearly indistinguishable from state-of-the-art algorithms for optimizing adversarial risk, but far more scalable and general. The experiments also confirm that without retraining, our adversarial framework is extremely effective in dramatically reducing the effectiveness of learning. In contrast, retraining significantly boosts robustness to evasion attacks without compromising much overall accuracy.

Connected through hyperedge HE-1 (induced by source paper "securing behavior based opinion spam detection"):
  with members:
    - "practical attacks against graph based clustering"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "temporal opinion spam detection by multivariate indicative signals"
    - "evasion attacks against machine learning at test time"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-2 (induced by source paper "adversarial classification on social networks"):
  with members:
    - "antisocial behavior in online discussion communities"
    - "influence maximization in continuous time diffusion networks"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-3 (induced by source paper "black box attacks against rnn based malware detection algorithms"):
  with members:
    - "generating adversarial malware examples for black box attacks based on gan"
    - "simple black box adversarial perturbations for deep networks"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"
    - "sequential short text classification with recurrent and convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "generating adversarial malware examples for black box attacks based on gan"):
  with members:
    - "adversarial perturbations against deep neural networks for malware classification"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "improved techniques for training gans"
    - "delving into transferable adversarial examples and black box attacks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "adam a method for stochastic optimization"

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.GT(Computer Science and Game Theory)