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: "asymptotic error free partitioning over noisy boolean multiaccess channels"
  Abstract: In this paper, we consider the problem of partitioning active users in a manner that facilitates multi-access without collision. The setting is of a noisy, synchronous, Boolean, and multi-access channel, where    $K$    active users (out of a total of    $N$    users) seek channel access. A solution to the partition problem places each of the    $N$    users in one of    $K$    groups (or blocks), such that no two active nodes are in the same block. We consider a simple, but non-trivial and illustrative, case of    $K=2$    active users and study the number of steps    $T$    used to solve the partition problem. By random coding and a suboptimal decoding scheme, we show that for any    $T\geq (C_{1} +\xi _{1})\log N$   , where    $C_{1}$    and    $\xi _{1}$    are positive constants (independent of    $N$   ), and where    $\xi _{1}$    can be arbitrary small, the partition problem can be solved with error probability    $P_{e}^{\vphantom {R^{A}}(N)} \to 0$   , for large    $N$   . Under the same scheme, we also bound    $T$    from the other direction, establishing that, for any    $T \leq (C_{2} - \xi _{2}) \log N$   , the error probability    $P_{e}^{(N)} \to 1$    for large    $N$   ; again,    $C_{2}$    and    $\xi _{2}$    are constants, and    $\xi _{2}$    can be arbitrarily small. These bounds on the number of steps are lower than the tight achievable lower bound in terms of    $T \geq (C_{g} +\xi )\log N $    for group testing (in which all active users are identified, rather than just partitioned). Thus, partitioning may prove to be a more efficient approach for multi-access than group testing.

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)