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: "neighbor discovery in wireless networks a multiuser detection approach"
  Abstract: We examine the problem of determining which nodes are neighbors of a given one in a wireless network. We consider an unsupervised network operating on a frequency-flat Gaussian channel, where $K+1$ nodes associate their identities to nonorthogonal signatures, transmitted at random times, synchronously, and independently. A number of neighbor-discovery algorithms, based on different optimization criteria, are introduced and analyzed. Numerical results show how reduced-complexity algorithms can achieve a satisfactory performance.

Connected through hyperedge HE-1 (induced by source paper "sparse ofdm a compressive sensing approach to asynchronous neighbor discovery"):
  with members:
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "on frame asynchronous coded slotted aloha asymptotic finite length and delay analysis"
    - "detection and combining techniques for asynchronous random access with time diversity"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "probabilistic rateless multiple access for machine to machine communication"
    - "many access channels the gaussian case with random user activities"
    - "asynchronous code division random access using convex optimization"
    - "neighbor discovery in a wireless sensor network multipacket reception capability and physical layer signal processing"

Connected through hyperedge HE-2 (induced by source paper "virtual full duplex wireless communication via rapid on off division duplex"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "capacity for half duplex line networks with two sources"
    - "asynchronous cdma systems with random spreading part ii design criteria"
    - "rethinking information theory for mobile ad hoc networks"
    - "coding strategies for noise free relay cascades with half duplex constraint"

Connected through hyperedge HE-3 (induced by source paper "neighbor discovery for wireless networks via compressed sensing"):
  with members:
    - "boolean compressed sensing and noisy group testing"
    - "graph constrained group testing"
    - "derandomization and group testing"
    - "compressed sensing with probabilistic measurements a group testing solution"
    - "list decoding of noisy reed muller like codes"

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)