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 "on percolation and np hardness"):
  Abstract: We consider the robustness of computational hardness of problems whose input is obtained by applying independent random deletions to worst-case instances. For some classical $NP$-hard problems on graphs, such as Coloring, Vertex-Cover, and Hamiltonicity, we examine the complexity of these problems when edges (or vertices) of an arbitrary graph are deleted independently with probability $1-p > 0$. We prove that for $n$-vertex graphs, these problems remain as hard as in the worst-case, as long as $p > \frac{1}{n^{1-\epsilon}}$ for arbitrary $\epsilon \in (0,1)$, unless $NP \subseteq BPP$. #R##N#We also prove hardness results for Constraint Satisfaction Problems, where random deletions are applied to clauses or variables, as well as the Subset-Sum problem, where items of a given instance are deleted at random.

Includes members:
  Member "on independent sets in random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the average case complexity of counting cliques in erdos renyi hypergraphs")
  Member "improved hardness of approximating chromatic number":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved hardness for h colourings of g colourable graphs")
  Member "recoverable values for independent sets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on giant components and treewidth in the layers model")
  Member "computational transition at the uniqueness threshold":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectral independence in high dimensional expanders and applications to the hardcore model")
  Member "subsampling mathematical relaxations and average case complexity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sublinear algorithms for maxcut and correlation clustering")
  Member "conditional hardness for approximate coloring":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the generalized orthogonality dimension of generalized kneser graphs bounds and applications")
  Member "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)