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 "nearest neighbor decoding for tardos fingerprinting codes"):
  Abstract: Over the past decade, various improvements have been made to Tardos' collusion-resistant fingerprinting scheme [Tardos, STOC 2003], ultimately resulting in a good understanding of what is the minimum code length required to achieve collusion-resistance. In contrast, decreasing the cost of the actual decoding algorithm for identifying the potential colluders has received less attention, even though previous results have shown that using joint decoding strategies, deemed too expensive for decoding, may lead to better code lengths. Moreover, in dynamic settings a fast decoder may be required to provide answers in real-time, further raising the question whether the decoding costs of score-based fingerprinting schemes can be decreased with a smarter decoding algorithm. In this paper we show how to model the decoding step of score-based fingerprinting as a nearest neighbor search problem, and how this relation allows us to apply techniques from the field of (approximate) nearest neighbor searching to obtain decoding times which are sublinear in the total number of users. As this does not affect the encoding and embedding steps, this decoding mechanism can easily be deployed within existing fingerprinting schemes, and this may bring a truly efficient joint decoder closer to reality. Besides the application to fingerprinting, similar techniques can potentially be used to decrease the decoding costs of group testing methods, which may be of independent interest.

Includes members:
  Member "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "optimal hashing based time space trade offs for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "optimal sequential fingerprinting wald vs tardos":
    also connected through other hyperedges:
      (none)
  Member "practical and optimal lsh for angular distance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "locality sensitive hashing without false negatives":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "similarity problems in high dimensions")
  Member "optimal symmetric tardos traitor tracing schemes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asymptotics of fingerprinting and group testing capacity achieving log likelihood decoders")
  Member "capacities and capacity achieving decoders for various fingerprinting games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "asymptotics of fingerprinting and group testing capacity achieving log likelihood decoders")
  Member "enhanced blind decoding of tardos codes with new map based functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "asymptotics of fingerprinting and group testing capacity achieving log likelihood decoders")

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)