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: "asymptotics of fingerprinting and group testing tight bounds from channel capacities"
  Abstract: In this paper, we consider the large-coalition asymptotics of various fingerprinting and group testing games, and derive explicit expressions for the capacities for each of these models. We do this both for simple (fast but suboptimal) and arbitrary, joint decoders (slow but optimal). For fingerprinting, we show that if the pirate strategy is known, the capacity often decreases linearly with the number of colluders, instead of quadratically as in the uninformed fingerprinting game. For all considered attacks, the joint capacity is shown to be strictly higher than the simple capacity, including the interleaving attack. This last result contradicts the results of Huang and Moulin regarding the joint fingerprinting capacity, which implies that finding the fingerprinting capacity without restrictions on the tracer’s capabilities remains an open problem. For group testing, we improve upon the previous work about the joint capacities, and derive new explicit asymptotics for the simple capacities of various models. These show that the existing simple group testing algorithms of Chan  et al.  are suboptimal, and that simple decoders cannot asymptotically be as efficient as joint decoders. For the traditional group testing model, we show that the gap between the simple and joint capacities is a factor    $\log _{2}(e) \approx 1.44$    for large numbers of defectives.

Connected through hyperedge HE-1 (induced by source paper "group testing an information theory perspective"):
  with members:
    - "quantitative group testing in the sublinear regime"
    - "understanding phase transitions via mutual information and mmse"
    - "performance of group testing algorithms with near constant tests per item"
    - "learning erd h o s r enyi random graphs via edge detecting queries"
    - "information theoretic and algorithmic thresholds for group testing"
    - "novel impossibility results for group testing"
    - "noisy adaptive group testing bounds and algorithms"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-2 (induced by source paper "near optimal noisy group testing via separate decoding of items"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "the capacity of bernoulli nonadaptive group testing"
    - "on the optimality of some group testing algorithms"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "computationally tractable algorithms for finding a subset of non defective items from a large population"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "group testing algorithms bounds and simulations"

Connected through hyperedge HE-3 (induced by source paper "asymptotics of fingerprinting and group testing capacity achieving log likelihood decoders"):
  with members:
    - "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"
    - "dynamic tardos traitor tracing schemes"
    - "on the saddle point solution and the large coalition asymptotics of fingerprinting games"

Connected through hyperedge HE-4 (induced by source paper "converse bounds for noisy group testing with arbitrary measurement matrices"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "strong converses for group testing in the finite blocklength regime"
    - "the capacity of adaptive group testing"
    - "boolean compressed sensing and noisy group testing"
    - "unbounded contention resolution in multiple access channels"

Connected through hyperedge HE-5 (induced by source paper "limits on support recovery with probabilistic models an information theoretic framework"):
  with members:
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "signal estimation with additive error metrics in compressed sensing"
    - "on the dispersions of three network information theory problems"
    - "group testing algorithms bounds and simulations"
    - "living on the edge phase transitions in convex programs with random data"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "the capacity of adaptive group testing"
    - "support recovery with sparsely sampled free random matrices"

Connected through hyperedge HE-6 (induced by source paper "optimal sequential fingerprinting wald vs tardos"):
  with members:
    - "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"
    - "efficient probabilistic group testing based on traitor tracing"

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.IT(Information Theory)