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: "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.

Connected through hyperedge HE-1 (induced by source paper "approximation algorithms for partially colorable graphs"):
  with members:
    - "a new algorithm for the robust semi random independent set problem"
    - "on the effect of randomness on planted 3 coloring models"
    - "combinatorial coloring of 3 colorable graphs"
    - "faster parameterized algorithms using linear programming"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "approximate graph coloring by semidefinite programming"

Connected through hyperedge HE-2 (induced by source paper "exact recovery for a family of community detection generative models"):
  with members:
    - "community detection in hypergraphs spiked tensor models and sum of squares"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-3 (induced by source paper "sever a robust meta algorithm for stochastic optimization"):
  with members:
    - "efficient algorithms and lower bounds for robust linear regression"
    - "robust physical world attacks on deep learning models"
    - "certified defenses for data poisoning attacks"
    - "learning geometric concepts with nasty noise"
    - "outlier robust moment estimation via sum of squares"
    - "being robust in high dimensions can be practical"
    - "meta qsar a large scale application of meta learning to drug design and discovery"
    - "the consciousness prior"

Connected through hyperedge HE-4 (induced by source paper "a new algorithm for the robust semi random independent set problem"):
  with members:
    - "learning from untrusted data"
    - "constant factor approximation for balanced cut in the pie model"
    - "grothendieck type inequalities in combinatorial optimization"
    - "how to play unique games against a semi random adversary"

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.CC(Computational Complexity)