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: "lectures on designing screening experiments"
  Abstract: Designing Screening Experiments (DSE) is a class of information - theoretical models for multiple - access channels (MAC). We discuss the combinatorial model of DSE called a disjunct channel model. This model is the most important for applications and closely connected with the superimposed code concept. We give a detailed survey of lower and upper bounds on the rate of superimposed codes. The best known constructions of superimposed codes are considered in paper. We also discuss the development of these codes (non-adaptive pooling designs) intended for the clone - library screening problem. We obtain lower and upper bounds on the rate of binary codes for the combinatorial model of DSE called an adder channel model. We also consider the concept of universal decoding for the probabilistic DSE model called a symmetric model of DSE.

Connected through hyperedge HE-1 (induced by source paper "optimal multistage group testing algorithm for 3 defectives"):
  with members:
    - "a new algorithm for two stage group testing"
    - "performance of group testing algorithms with near constant tests per item"
    - "on a hypergraph approach to multistage group testing problems"
    - "bounds on the rate of disjunctive codes"
    - "group testing with random pools optimal two stage algorithms"

Connected through hyperedge HE-2 (induced by source paper "signature codes for weighted binary adder channel and multimedia fingerprinting"):
  with members:
    - "codes with the identifiable parent property for multimedia fingerprinting"
    - "probabilistic existence results for separable codes"
    - "strongly separable codes"
    - "two level fingerprinting codes"

Connected through hyperedge HE-3 (induced by source paper "a new algorithm for two stage group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "on a hypergraph approach to multistage group testing problems"
    - "bounds on the rate of disjunctive codes"
    - "group testing with random pools optimal two stage algorithms"

Connected through hyperedge HE-4 (induced by source paper "bounds for signature codes"):
  with members:
    - "probabilistic existence results for separable codes"
    - "new bounds for frameproof codes"

Connected through hyperedge HE-5 (induced by source paper "on a hypergraph approach to multistage group testing problems"):
  with members:
    - "bounds on the rate of disjunctive codes in russian"
    - "bounds on the rate of disjunctive codes"
    - "group testing with random pools optimal two stage algorithms"

Connected through hyperedge HE-6 (induced by source paper "poisson group testing a probabilistic model for boolean compressed sensing"):
  with members:
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "group testing with prior statistics"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "semiquantitative group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "semi quantitative group testing"

Connected through hyperedge HE-7 (induced by source paper "sparse recovery with linear and nonlinear observations dependent and noisy data"):
  with members:
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "optimal phase transitions in compressed sensing"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "information theoretic bounds for compressed sensing"
    - "necessary and sufficient conditions for sparsity pattern recovery"

Connected through hyperedge HE-8 (induced by source paper "semiquantitative group testing"):
  with members:
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "semi quantitative group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "improved constructions for non adaptive threshold group testing"
    - "note on noisy group testing asymptotic bounds and belief propagation reconstruction"
    - "weighted superimposed codes and constrained integer compressed sensing"
    - "fast min sum algorithms for decoding of ldpc over gf q"

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)