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: "partition information and its transmission over boolean multi access channels"
  Abstract: In this paper, we propose a novel reservation system to study partition information and its transmission over a noise-free Boolean multiaccess channel. The objective of transmission is not to restore the message, but to partition active users into distinct groups so that they can, subsequently, transmit their messages without collision. We first calculate (by mutual information) the amount of information needed for the partitioning without channel effects, and then propose two different coding schemes to obtain achievable transmission rates over the channel. The first one is the brute force method, where the codebook design is based on centralized source coding; the second method uses random coding, where the codebook is generated randomly and optimal Bayesian decoding is employed to reconstruct the partition. Both methods shed light on the internal structure of the partition problem. A novel formulation is proposed for the random coding scheme, in which a sequence of channel operations and interactions induces a hypergraph. The formulation intuitively describes the transmitted information in terms of a strong coloring of this hypergraph. An extended Fibonacci structure is constructed for the simple, but nontrivial, case with two active users. A comparison between these methods and group testing is conducted to demonstrate the potential of our approaches.

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 "performance of group testing algorithms with near constant tests per item"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "on the optimality of some group testing algorithms"
    - "improved group testing rates with constant column weight designs"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "group testing algorithms bounds and simulations"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-3 (induced by source paper "data aggregation over multiple access wireless sensors network"):
  with members:
    - "secure group testing"
    - "near optimal adaptive compressed sensing"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-4 (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"
    - "information theoretic bounds for adaptive sparse recovery"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-5 (induced by source paper "asymptotic error free partitioning over noisy boolean multiaccess channels"):
  with members:
    - "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)