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: "data poisoning attacks against online learning"
  Abstract: We consider data poisoning attacks, a class of adversarial attacks on machine learning where an adversary has the power to alter a small fraction of the training data in order to make the trained classifier satisfy certain objectives. While there has been much prior work on data poisoning, most of it is in the offline setting, and attacks for online learning, where training data arrives in a streaming manner, are not well understood. #R##N#In this work, we initiate a systematic investigation of data poisoning attacks for online learning. We formalize the problem into two settings, and we propose a general attack strategy, formulated as an optimization problem, that applies to both with some modifications. We propose three solution strategies, and perform extensive experimental evaluation. Finally, we discuss the implications of our findings for building successful defenses.

Connected through hyperedge HE-1 (induced by source paper "on defending against label flipping attacks on malware detection systems"):
  with members:
    - "can machine learning model with static features be fooled an adversarial machine learning approach"
    - "beyond supervised classification extreme minimal supervision with the graph 1 laplacian"
    - "label propagation for deep semi supervised learning"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "is feature selection secure against training data poisoning"
    - "learning to reweight examples for robust deep learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "enhancing robustness of machine learning systems via data transformations"

Connected through hyperedge HE-2 (induced by source paper "shapley homology topological analysis of sample influence for neural networks"):
  with members:
    - "dataset distillation"
    - "adversarial attack on graph structured data"
    - "verification of recurrent neural networks through rule extraction"
    - "representer point selection for explaining deep neural networks"
    - "learning to reweight examples for robust deep learning"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "approximating the spectrum of a graph"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-3 (induced by source paper "a unified framework for data poisoning attack to graph based semi supervised learning"):
  with members:
    - "adversarial attack on graph structured data"
    - "implicit reparameterization gradients"
    - "attack graph convolutional networks by adding fake nodes"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "crafting adversarial input sequences for recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "multiparty dynamics and failure modes for machine learning and artificial intelligence"):
  with members:
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "oversight of unsafe systems via dynamic safety envelopes"
    - "how do classifiers induce agents to invest effort strategically"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "supervising strong learners by amplifying weak experts"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "robustness to fundamental uncertainty in agi alignment"
    - "multi agent reinforcement learning in sequential social dilemmas"

Connected through hyperedge HE-5 (induced by source paper "online data poisoning attack"):
  with members:
    - "optimal adversarial attack on autoregressive models"
    - "online control with adversarial disturbances"
    - "training set camouflage"
    - "an optimal control approach to sequential machine teaching"
    - "online linear quadratic control"
    - "an optimal control view of adversarial machine learning"
    - "data poisoning attacks in contextual bandits"
    - "towards poisoning of deep learning algorithms with back gradient optimization"

Connected through hyperedge HE-6 (induced by source paper "data poisoning attacks on stochastic bandits"):
  with members:
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "adversarial attacks on stochastic bandits"
    - "stochastic bandits robust to adversarial corruptions"
    - "ucboost a boosting approach to tame complexity and optimality for stochastic bandits"
    - "data poisoning attacks in contextual bandits"
    - "is feature selection secure against training data poisoning"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"

Connected through hyperedge HE-7 (induced by source paper "policy poisoning in batch reinforcement learning and control"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "data poisoning attacks on stochastic bandits"
    - "online data poisoning attack"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "data poisoning attacks in contextual bandits"
    - "is feature selection secure against training data poisoning"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-8 (induced by source paper "an investigation of data poisoning defenses for online learning"):
  with members:
    - "online data poisoning attack"
    - "an optimal control approach to sequential machine teaching"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "sever a robust meta algorithm for stochastic optimization"
    - "data poisoning attacks in contextual bandits"
    - "certified defenses for data poisoning attacks"
    - "being robust in high dimensions can be practical"
    - "learning under p tampering 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)