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: "multi party poisoning through generalized p tampering"
  Abstract: In a poisoning attack against a learning algorithm, an adversary tampers with a fraction of the training data $T$ with the goal of increasing the classification error of the constructed hypothesis/model over the final test distribution. In the distributed setting, $T$ might be gathered gradually from $m$ data providers $P_1,\dots,P_m$ who generate and submit their shares of $T$ in an online way. #R##N#In this work, we initiate a formal study of $(k,p)$-poisoning attacks in which an adversary controls $k\in[n]$ of the parties, and even for each corrupted party $P_i$, the adversary submits some poisoned data $T'_i$ on behalf of $P_i$ that is still "$(1-p)$-close" to the correct data $T_i$ (e.g., $1-p$ fraction of $T'_i$ is still honestly generated). For $k=m$, this model becomes the traditional notion of poisoning, and for $p=1$ it coincides with the standard notion of corruption in multi-party computation. #R##N#We prove that if there is an initial constant error for the generated hypothesis $h$, there is always a $(k,p)$-poisoning attacker who can decrease the confidence of $h$ (to have a small error), or alternatively increase the error of $h$, by $\Omega(p \cdot k/m)$. Our attacks can be implemented in polynomial time given samples from the correct data, and they use no wrong labels if the original distributions are not noisy. #R##N#At a technical level, we prove a general lemma about biasing bounded functions $f(x_1,\dots,x_n)\in[0,1]$ through an attack model in which each block $x_i$ might be controlled by an adversary with marginal probability $p$ in an online way. When the probabilities are independent, this coincides with the model of $p$-tampering attacks, thus we call our model generalized $p$-tampering. We prove the power of such attacks by incorporating ideas from the context of coin-flipping attacks into the $p$-tampering model and generalize the results in both of these areas.

Connected through hyperedge HE-1 (induced by source paper "eavesdrop the composition proportion of training labels in federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "a little is enough circumventing defenses for distributed learning"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "federated learning for emoji prediction in a mobile keyboard"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "how to backdoor federated learning"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "towards federated learning at scale system design"
    - "contamination attacks and mitigation in multi party machine learning"
    - "a little is enough circumventing defenses for distributed learning"
    - "distributed training with heterogeneous data bridging median and mean based algorithms"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "analyzing federated learning through an adversarial lens"
    - "fine pruning defending against backdooring attacks on deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "can adversarially robust learning leverage computational hardness"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "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"
    - "are adversarial examples inevitable"
    - "adversarially robust generalization requires more data"
    - "poison frogs targeted clean label poisoning attacks on neural networks"

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)