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: "non malleable extractors and non malleable codes partially optimal constructions"
  Abstract: The recent line of study on randomness extractors has been a great success, resulting in exciting new techniques, new connections, and breakthroughs to long standing open problems in several seemingly different topics. These include seeded non-malleable extractors, privacy amplification protocols with an active adversary, independent source extractors (and explicit Ramsey graphs), and non-malleable codes in the split state model. However, in all cases there is still a gap to optimum and the motivation to close this gap remains strong. #R##N#In this paper, we introduce a set of new techniques to further push the frontier in the above questions. Our techniques lead to improvements in all of the above questions, and in several cases partially optimal constructions. Specifically, we obtain: 1. A seeded non-malleable extractor with seed length $O(log n)+log^{1+o(1)}(1/\epsilon) and entropy requirement O(log log n+log(1/\epsilon)), where the entropy requirement is asymptotically optimal by a recent result of Gur and Shinkar \cite{GurS17}; 2. A two-round privacy amplification protocol with optimal entropy loss for security parameter up to \Omega(k), which solves the privacy amplification problem completely; 3. A two-source extractor for entropy O(\frac{log n log log n}{log log log n}), which also gives an explicit Ramsey graph on N vertices with no clique or independent set of size (log N)^{O(\frac{log log log N}{log log log log N})}; and 4. The first explicit non-malleable code in the 2-split state model with \emph{constant} rate, which has been a major goal in the study of non-malleable codes for quite some time. One small caveat is that the error of this code is only (an arbitrarily small) constant, but we can also achieve negligible error with rate \Omega(log log log n/log log n), which already improves the rate in \cite{Li17} exponentially.

Connected through hyperedge HE-1 (induced by source paper "lower bounds for matrix factorization"):
  with members:
    - "static data structure lower bounds imply rigidity"
    - "matrix rigidity and the croot lev pach lemma"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "probabilistic rank and matrix rigidity"
    - "hitting sets for roabp and sum of set multilinear circuits"
    - "improved bounds for reduction to depth 4 and depth 3"
    - "on identity testing of tensors low rank recovery and compressed sensing"
    - "the cell probe complexity of dynamic range counting"

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)