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: "adaptive group testing as channel coding with feedback"
  Abstract: Group testing is the combinatorial problem of identifying the defective items in a population by grouping items into test pools. Recently, nonadaptive group testing — where all the test pools must be decided on at the start — has been studied from an information theory point of view. Using techniques from channel coding, upper and lower bounds have been given on the number of tests required to accurately recover the defective set, even when the test outcomes can be noisy. In this paper, we give the first information-theoretic result on adaptive group testing — where the outcome of previous tests can influence the makeup of future tests. We show that adaptive testing does not help much, as the number of tests required obeys the same lower bound as nonadaptive testing. Our proof uses similar techniques to the proof that feedback does not improve channel capacity.

Connected through hyperedge HE-1 (induced by source paper "secure adaptive group testing"):
  with members:
    - "data aggregation over multiple access wireless sensors network"
    - "secure group testing"
    - "information theoretic bounds for adaptive sparse recovery"
    - "the capacity of non identical adaptive group testing"
    - "near optimal adaptive compressed sensing"
    - "wiretap channel with causal state information and secure rate limited feedback"
    - "the capacity of adaptive group testing"
    - "boolean compressed sensing and noisy group testing"

Connected through hyperedge HE-2 (induced by source paper "secure group testing"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "strong converses for group testing in the finite blocklength regime"
    - "partition information and its transmission over boolean multi access channels"
    - "information theoretic bounds for adaptive sparse recovery"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"

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"
    - "dynamic tardos traitor tracing schemes"

Connected through hyperedge HE-4 (induced by source paper "strong converses for group testing in the finite blocklength regime"):
  with members:
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "group testing with prior statistics"
    - "bitwise map algorithm for group testing based on holographic transformation"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "variable length compression allowing errors extended"
    - "variable length compression allowing errors"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-5 (induced by source paper "group testing algorithms bounds and simulations"):
  with members:
    - "non adaptive group testing based on sparse pooling graphs"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "the capacity of adaptive group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "note on noisy group testing asymptotic bounds and belief propagation reconstruction"

Connected through hyperedge HE-6 (induced by source paper "information theoretic bounds for adaptive sparse recovery"):
  with members:
    - "sparse recovery with linear and nonlinear observations dependent and noisy data"
    - "near optimal adaptive compressed sensing"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "the capacity of adaptive group testing"
    - "boolean compressed sensing and noisy group testing"
    - "information theoretic bounds for compressed sensing"

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"
    - "dynamic tardos traitor tracing schemes"
    - "on the saddle point solution and the large coalition asymptotics of fingerprinting games"
    - "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 "the capacity of adaptive group testing"):
  with members:
    - "group testing algorithms bounds and simulations"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "boolean compressed sensing and noisy group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"

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)