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: "discrete distributions in the tardos scheme revisited"
  Abstract: The Tardos scheme is a well-known traitor tracing scheme to protect copyrighted content against collusion attacks. The original scheme contained some suboptimal design choices, such as the score function and the distribution function used for generating the biases. Skoric et al. previously showed that a symbol-symmetric score function leads to shorter codes, while Nuida et al. obtained the optimal distribution functions for arbitrary coalition sizes. Later, Nuida et al. showed that combining these results leads to even shorter codes when the coalition size is small. We extend their analysis to the case of large coalitions and prove that these optimal distributions converge to the arcsine distribution, thus showing that the arcsine distribution is asymptotically optimal in the symmetric Tardos scheme. We also present a new, practical alternative to the discrete distributions of Nuida et al. and give a comparison of the estimated lengths of the fingerprinting codes for each of these distributions.

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"
    - "efficient probabilistic group testing based on traitor tracing"
    - "dynamic tardos traitor tracing schemes"
    - "on the saddle point solution and the large coalition asymptotics of fingerprinting games"

Connected through hyperedge HE-3 (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"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "efficient probabilistic group testing based on traitor tracing"

Connected through hyperedge HE-4 (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"
    - "efficient probabilistic group testing based on traitor tracing"
    - "dynamic tardos traitor tracing schemes"
    - "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-5 (induced by source paper "dynamic traitor tracing schemes revisited"):
  with members:
    - "optimal symmetric tardos traitor tracing schemes"
    - "efficient probabilistic group testing based on traitor tracing"
    - "dynamic tardos traitor tracing schemes"
    - "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)