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: "learning under p tampering attacks"
  Abstract: Mahloujifar and Mahmoody (TCC'17) studied attacks against learning algorithms using a special case of Valiant's malicious noise, called $p$-tampering, in which the adversary could change training examples with independent probability $p$ but only using correct labels. They showed the power of such attacks by increasing the error probability in the so called `targeted' poisoning model in which the adversary's goal is to increase the loss of the generated hypothesis over a particular test example. At the heart of their attack was an efficient algorithm to bias the average output of any bounded real-valued function through $p$-tampering. #R##N#In this work, we present new attacks for biasing the average output of bounded real-valued functions, improving upon the biasing attacks of MM16. Our improved biasing attacks, directly imply improved $p$-tampering attacks against learners in the targeted poisoning model. As a bonus, our attacks come with considerably simpler analysis compared to previous attacks. #R##N#We also study the possibility of PAC learning under $p$-tampering attacks in the \emph{non-targeted} (aka indiscriminate) setting where the adversary's goal is to increase the risk of the generated hypothesis (for a random test example). We show that PAC learning is \emph{possible} under $p$-tampering poisoning attacks essentially whenever it is possible in the realizable setting without the attacks. We further show that PAC learning under `no-mistake' adversarial noise is \emph{not} possible, if the adversary could choose the (still limited to only $p$ fraction of) tampered examples that she substitutes with adversarially chosen ones. Our formal model for such `bounded-budget' tampering attackers is inspired by the notions of (strong) adaptive corruption in secure multi-party computation.

Connected through hyperedge HE-1 (induced by source paper "lower bounds for adversarially robust pac learning"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "empirically measuring concentration fundamental limits on intrinsic robustness"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "adversarial spheres"
    - "adversarial examples from cryptographic pseudo random generators"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "improved generalization bounds for robust learning"

Connected through hyperedge HE-2 (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"
    - "data poisoning attacks against online learning"
    - "certified defenses for data poisoning attacks"
    - "being robust in high dimensions can be practical"

Connected through hyperedge HE-3 (induced by source paper "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"):
  with members:
    - "efficient algorithms and lower bounds for robust linear regression"
    - "adversarial vulnerability for any classifier"
    - "sever a robust meta algorithm for stochastic optimization"
    - "improved generalization bounds for robust learning"
    - "adversarially robust generalization requires more data"
    - "sok security and privacy in machine learning"
    - "data poisoning attacks against online learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "poison frogs targeted clean label poisoning attacks on neural networks"):
  with members:
    - "when does machine learning fail generalized transferability for evasion and poisoning attacks"
    - "certified defenses for data poisoning attacks"
    - "generative poisoning attack method against neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "efficient robust proper learning of log concave distributions"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

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)