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 "does robustness imply tractability a lower bound for planted clique in the semi random model"):
  Abstract: We consider a robust analog of the planted clique problem. In this analog, a set $S$ of vertices is chosen and all edges in $S$ are included; then, edges between $S$ and the rest of the graph are included with probability $\frac{1}{2}$, while edges not touching $S$ are allowed to vary arbitrarily. For this semi-random model, we show that the information-theoretic threshold for recovery is $\tilde{\Theta}(\sqrt{n})$, in sharp contrast to the classical information-theoretic threshold of $\Theta(\log(n))$. This matches the conjectured computational threshold for the classical planted clique problem, and thus raises the intriguing possibility that, once we require robustness, there is no computational-statistical gap for planted clique. Our lower bound involves establishing a result regarding the KL divergence of a family of perturbed Bernoulli distributions, which may be of independent interest.

Includes members:
  Member "a new algorithm for the robust semi random independent set problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximation algorithms for partially colorable graphs")
  Member "multisection in the stochastic block model using semidefinite programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "planted models for k way edge and vertex expansion")
  Member "resilience a criterion for learning in the presence of arbitrary outliers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning structured distributions from untrusted batches faster and simpler")
  Member "learning from untrusted data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "how robust are reconstruction thresholds for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improper learning for non stochastic control")

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.CC(Computational Complexity)