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 codes with their many tampered extensions"):
  Abstract: Randomness extractors and error correcting codes are fundamental objects in computer science. Recently, there have been several natural generalizations of these objects, in the context and study of tamper resilient cryptography. These are seeded non-malleable extractors, introduced in [DW09]; seedless non-malleable extractors, introduced in [CG14b]; and non-malleable codes, introduced in [DPW10]. #R##N#However, explicit constructions of non-malleable extractors appear to be hard, and the known constructions are far behind their non-tampered counterparts. #R##N#In this paper we make progress towards solving the above problems. Our contributions are as follows. #R##N#(1) We construct an explicit seeded non-malleable extractor for min-entropy $k \geq \log^2 n$. This dramatically improves all previous results and gives a simpler 2-round privacy amplification protocol with optimal entropy loss, matching the best known result in [Li15b]. #R##N#(2) We construct the first explicit non-malleable two-source extractor for min-entropy $k \geq n-n^{\Omega(1)}$, with output size $n^{\Omega(1)}$ and error $2^{-n^{\Omega(1)}}$. #R##N#(3) We initiate the study of two natural generalizations of seedless non-malleable extractors and non-malleable codes, where the sources or the codeword may be tampered many times. We construct the first explicit non-malleable two-source extractor with tampering degree $t$ up to $n^{\Omega(1)}$, which works for min-entropy $k \geq n-n^{\Omega(1)}$, with output size $n^{\Omega(1)}$ and error $2^{-n^{\Omega(1)}}$. We show that we can efficiently sample uniformly from any pre-image. By the connection in [CG14b], we also obtain the first explicit non-malleable codes with tampering degree $t$ up to $n^{\Omega(1)}$, relative rate $n^{\Omega(1)}/n$, and error $2^{-n^{\Omega(1)}}$.

Includes members:
  Member "three source extractors for polylogarithmic min entropy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non malleable extractors and non malleable codes partially optimal constructions")
  Member "non malleable coding against bit wise and split state tampering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "leakage resilient non malleable secret sharing in non compartmentalized models")
  Member "capacity of non malleable codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improved non malleable extractors non malleable codes and independent source extractors")
  Member "non malleable condensers for arbitrary min entropy and almost optimal protocols for privacy amplification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved non malleable extractors non malleable codes and independent source extractors")
  Member "non malleable extractors two source extractors and privacy amplification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "leakage resilient non malleable secret sharing in non compartmentalized models")
  Member "non malleable codes from the wire tap channel":
    also connected through other hyperedges:
      (none)
  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.CR(Cryptography and Security)