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: "multi cell aware opportunistic random access for machine type communications"
  Abstract: Due to the difficulty of coordination in multi-cell random access, it is a practical challenge how to achieve the optimal throughput with decentralized transmission. In this paper, we propose a decentralized multi-cell-aware opportunistic random access (MA-ORA) protocol that achieves the optimal throughput scaling in an ultra-dense $K$-cell random access network with one access point (AP) and $N$ users in each cell, which is suited for machine-type communications. Unlike opportunistic scheduling for cellular multiple access where users are selected by base stations, under our MA-ORA protocol, each user opportunistically transmits with a predefined physical layer data rate in a decentralized manner if the desired signal power to the serving AP is sufficiently large and the generating interference leakage power to the other APs is sufficiently small (i.e., two threshold conditions are fulfilled). As a main result, it is proved that the aggregate throughput scales as $\frac{K}{e}(1-\epsilon) \log (\textsf{snr} \log N)$ in a high signal-to-noise ratio (SNR) regime if $N$ scales faster than $\textsf{snr}^{\frac{K-1}{1-\delta}}$ for small constants $\epsilon, \delta>0$. Our analytical result is validated by computer simulations. In addition, numerical evaluation confirms that under a practical setting, the proposed MA-ORA protocol outperforms conventional opportunistic random access protocols in terms of throughput.

Connected through hyperedge HE-1 (induced by source paper "multi stream opportunistic network decoupling relay selection and interference management"):
  with members:
    - "opportunistic downlink interference alignment for multi cell mimo networks"
    - "real interference alignment exploiting the potential of single antenna systems"
    - "advanced interference management technique potentials and limitations"
    - "parallel opportunistic routing in wireless networks"
    - "opportunistic interference alignment for mimo interfering multiple access channels"
    - "multi cell random beamforming achievable rate and degrees of freedom region"
    - "opportunistic relaying in wireless networks"
    - "degrees of freedom of the k user m times n mimo interference channel"

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)