human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sparse ofdm a compressive sensing approach to asynchronous neighbor discovery"):
  Abstract: A novel low-complexity wireless neighbor discovery scheme, referred to as sparse orthogonal frequency division multiplexing (sparse-OFDM) is proposed. One area of application is the "Internet of Things" (IoT). The number of devices is very large while every device accesses the network with a small probability, so the number of active devices in a frame is much smaller than the total local device population. Sparse OFDM is a one-shot transmission scheme with low complexity, which exploits both the parallel channel access offered by OFDM and the bursty nature of transmissions. When the transmission delay of each device is an integer number of symbol intervals, analysis and simulation show that sparse OFDM enables successful asynchronous neighbor discovery using a much smaller code length than random access schemes.

Includes members:
  Member "massive connectivity with massive mimo part i device activity detection and channel estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "on frame asynchronous coded slotted aloha asymptotic finite length and delay analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reliability latency performance of frameless aloha with and without feedback")
  Member "detection and combining techniques for asynchronous random access with time diversity":
    also connected through other hyperedges:
      (none)
  Member "coded slotted aloha a graph based method for uncoordinated multiple access":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "phase transition analysis for covariance based massive random access with massive mimo")
  Member "probabilistic rateless multiple access for machine to machine communication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "massive access in beyond 5g iot networks with noma np hardness competitiveness and learning")
  Member "many access channels the gaussian case with random user activities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "asynchronous code division random access using convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on throughput of compressive random access for one short message delivery in iot")
  Member "neighbor discovery in a wireless sensor network multipacket reception capability and physical layer signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "topology discovery for linear wireless networks with application to train backbone inauguration")

Question: Please tell me which class the source paper 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)