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: "dynamic tardos traitor tracing schemes"
  Abstract: We construct binary dynamic traitor tracing schemes, where the number of watermark bits needed to trace and disconnect any coalition of pirates is quadratic in the number of pirates, and logarithmic in the total number of users and the error probability. Our results improve upon results of Tassa, and our schemes have several other advantages, such as being able to generate all codewords in advance, a simple accusation method, and flexibility when the feedback from the pirate network is delayed.

Connected through hyperedge HE-1 (induced by source paper "nearest neighbor decoding for tardos fingerprinting codes"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "optimal sequential fingerprinting wald vs tardos"
    - "practical and optimal lsh for angular distance"
    - "locality sensitive hashing without false negatives"
    - "optimal symmetric tardos traitor tracing schemes"
    - "capacities and capacity achieving decoders for various fingerprinting games"
    - "enhanced blind decoding of tardos codes with new map based functions"

Connected through hyperedge HE-2 (induced by source paper "asymptotics of fingerprinting and group testing capacity achieving log likelihood decoders"):
  with members:
    - "asymptotics of fingerprinting and group testing tight bounds from channel capacities"
    - "optimal symmetric tardos traitor tracing schemes"
    - "capacities and capacity achieving decoders for various fingerprinting games"
    - "enhanced blind decoding of tardos codes with new map based functions"
    - "dynamic traitor tracing schemes revisited"
    - "discrete distributions in the tardos scheme revisited"
    - "efficient probabilistic group testing based on traitor tracing"
    - "on the saddle point solution and the large coalition asymptotics of fingerprinting games"

Connected through hyperedge HE-3 (induced by source paper "asymptotics of fingerprinting and group testing tight bounds from channel capacities"):
  with members:
    - "asymptotics of fingerprinting and group testing capacity achieving log likelihood decoders"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "capacities and capacity achieving decoders for various fingerprinting games"
    - "near optimal stochastic threshold group testing"
    - "dynamic traitor tracing schemes revisited"
    - "the capacity of adaptive group testing"
    - "efficient probabilistic group testing based on traitor tracing"
    - "on the saddle point solution and the large coalition asymptotics of fingerprinting games"

Connected through hyperedge HE-4 (induced by source paper "optimal sequential fingerprinting wald vs tardos"):
  with members:
    - "asymptotics of fingerprinting and group testing tight bounds from channel capacities"
    - "optimal symmetric tardos traitor tracing schemes"
    - "capacities and capacity achieving decoders for various fingerprinting games"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"
    - "enhanced blind decoding of tardos codes with new map based functions"
    - "dynamic traitor tracing schemes revisited"
    - "discrete distributions in the tardos scheme revisited"
    - "fingerprinting codes and the price of approximate differential privacy"

Connected through hyperedge HE-5 (induced by source paper "capacities and capacity achieving decoders for various fingerprinting games"):
  with members:
    - "optimal symmetric tardos traitor tracing schemes"
    - "dynamic traitor tracing schemes revisited"
    - "discrete distributions in the tardos scheme revisited"
    - "efficient probabilistic group testing based on traitor tracing"
    - "on the saddle point solution and the large coalition asymptotics of fingerprinting games"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "dynamic traitor tracing for arbitrary alphabets divide and conquer"
    - "asymptotic fingerprinting capacity for non binary alphabets"

Connected through hyperedge HE-6 (induced by source paper "interactive fingerprinting codes and the hardness of preventing false discovery"):
  with members:
    - "between pure and approximate differential privacy"
    - "preventing false discovery in interactive data analysis is hard"
    - "analysis of boolean functions"
    - "preserving statistical validity in adaptive data analysis"
    - "private multiplicative weights beyond linear queries"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-7 (induced by source paper "efficient probabilistic group testing based on traitor tracing"):
  with members:
    - "optimal symmetric tardos traitor tracing schemes"
    - "near optimal stochastic threshold group testing"
    - "dynamic traitor tracing schemes revisited"
    - "the capacity of adaptive group testing"
    - "on the saddle point solution and the large coalition asymptotics of fingerprinting games"
    - "adaptive group testing as channel coding with feedback"
    - "boolean compressed sensing and noisy group testing"
    - "dynamic traitor tracing for arbitrary alphabets divide and conquer"

Connected through hyperedge HE-8 (induced by source paper "dynamic traitor tracing schemes revisited"):
  with members:
    - "optimal symmetric tardos traitor tracing schemes"
    - "discrete distributions in the tardos scheme revisited"
    - "efficient probabilistic group testing based on traitor tracing"
    - "on the saddle point solution and the large coalition asymptotics of fingerprinting games"
    - "dynamic traitor tracing for arbitrary alphabets divide and conquer"

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.CR(Cryptography and Security)