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: "sense and predict opportunistic mac based on spatial interference correlation for cognitive radio networks"
  Abstract: Opportunity detection at secondary transmitters (TXs) is a key technique enabling cognitive radio (CR) networks. Such detection however cannot guarantee reliable communication at secondary receivers (RXs), especially when their association distance is long. To cope with the issue, this paper proposes a novel MAC called sense-and-predict (SaP), where each secondary TX decides whether to access or not based on the prediction of the interference level at RX. Firstly, we provide the spatial interference correlation in a probabilistic form using stochastic geometry, and utilize it to maximize the area spectral efficiency (ASE) for secondary networks while guaranteeing the service quality of primary networks. Through simulations and testbed experiments using USRP, SaP is shown to always achieve ASE improvement compared with the conventional TX based sensing.

Connected through hyperedge HE-1 (induced by source paper "sense and predict harnessing spatial interference correlation for cognitive radio networks"):
  with members:
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "millimeter wave interference avoidance via building aware associations"
    - "the connectivity of millimeter wave networks in urban environments modeled using random lattices"
    - "macro diversity in cellular networks with random blockages"
    - "downlink and uplink cell association with traditional macrocells and millimeter wave small cells"
    - "spatio temporal interference correlation and joint coverage in cellular networks"
    - "design and analysis of initial access in millimeter wave cellular networks"
    - "cognitive random access for internet of things networks"

Connected through hyperedge HE-2 (induced by source paper "opportunism in dynamic spectrum access for 5g a concept and its application to duplexing"):
  with members:
    - "compact full duplex mimo radios in d2d underlaid cellular networks from system design to prototype results"
    - "prototyping real time full duplex radios"
    - "full duplex cognitive radio a new design paradigm for enhancing spectrum usage"

Connected through hyperedge HE-3 (induced by source paper "cognitive random access for internet of things networks"):
  with members:
    - "wide area wireless communication challenges for the internet of things"
    - "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)