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: "further properties of wireless channel capacity"
  Abstract: Future wireless communication calls for exploration of more efficient use of wireless channel capacity to meet the increasing demand on higher data rate and less latency. However, while the ergodic capacity and instantaneous capacity of a wireless channel have been extensively studied, they are in many cases not sufficient for use in assessing if data transmission over the channel meets the quality of service (QoS) requirements. To address this limitation, we advocate a set of wireless channel capacity concepts, namely "cumulative capacity", "maximum cumulative capacity", "minimum cumulative capacity", and "range of cumulative capacity", and for each, study its properties by taking into consideration the impact of the underlying dependence structure of the corresponding stochastic process. Specifically, their cumulative distribution function (CDFs) are investigated extensively, where copula is adopted to express the dependence structures. Results considering both generic and specific dependence structures are derived. In particular, in addition to i.i.d., a specially investigated dependence structure is comonotonicity, i.e, the time series of wireless channel capacity are increasing functions of a common random variable. Appealingly, copula can serve as a unifying technique for obtaining results under various dependence assumptions, e.g. i.i.d. and Markov dependence, which are widely seen in stochastic network calculus. Moreover, some other characterizations of cumulative capacity are also studied, including moment generating function, Mellin transform, and stochastic service curve. With these properties, we believe QoS assessment of data transmission over the channel can be further performed, e.g. by applying analytical techniques and results of the stochastic network calculus theory.

Connected through hyperedge HE-1 (induced by source paper "qos provisioning in large wireless networks"):
  with members:
    - "the meta distribution of the sir in poisson bipolar and cellular networks"
    - "managing interference correlation through random medium access"
    - "a new phase transitions for local delays in manets"
    - "spatial and temporal correlation of the interference in aloha ad hoc networks"

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)