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 "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.

Includes members:
  Member "an entropy lower bound for non malleable extractors":
    also connected through other hyperedges:
      (none)
  Member "improved non malleable extractors non malleable codes and independent source extractors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leakage resilient non malleable secret sharing in non compartmentalized models")
  Member "explicit non malleable extractors multi source extractors and almost optimal privacy amplification protocols":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved non malleable extractors non malleable codes and independent source extractors")
  Member "explicit resilient functions matching ajtai linial":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal streaming and tracking distinct elements with high probability")
  Member "non malleable extractors and codes with their many tampered extensions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "leakage resilient non malleable secret sharing in non compartmentalized models")
  Member "three source extractors for polylogarithmic min entropy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved non malleable extractors non malleable codes and independent source extractors")
  Member "fuzzy extractors how to generate strong keys from biometrics and other noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a capacity achieving one message key agreement with finite blocklength analysis")

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)