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: "asynchronous code division random access using convex optimization"
  Abstract: Abstract   Many applications in cellular systems and sensor networks involve a random subset of a large number of users asynchronously reporting activity to a base station. This paper examines the problem of multiuser detection (MUD) in random access channels for such applications. Traditional orthogonal signaling ignores the random nature of user activity in this problem and limits the total number of users to be on the order of the number of signal space dimensions. Contention-based schemes, on the other hand, suffer from delays caused by colliding transmissions and the hidden node problem. In contrast, this paper presents a novel pairing of an asynchronous non-orthogonal code-division random access scheme with a convex optimization-based MUD algorithm that overcomes the issues associated with orthogonal signaling and contention-based methods. Two key distinguishing features of the proposed MUD algorithm are that it does not require knowledge of the delay or channel state information of every user and it has polynomial-time computational complexity. The main analytical contribution of this paper is the relationship between the performance of the proposed MUD algorithm in the presence of arbitrary or random delays and two simple metrics of the set of user codewords. The study of these metrics is then focused on two specific sets of codewords, random binary codewords and specially constructed algebraic codewords, for asynchronous random access. The ensuing analysis confirms that the proposed scheme together with either of these two codeword sets significantly outperforms the orthogonal signaling-based random access in terms of the total number of users in the system.

Connected through hyperedge HE-1 (induced by source paper "on throughput of compressive random access for one short message delivery in iot"):
  with members:
    - "massive mimo has unlimited capacity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "random pilot and data access in massive mimo for machine type communications"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "massive mimo for wireless sensing with a coherent multiple access channel"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-2 (induced by source paper "multichannel aloha with exploration phase"):
  with members:
    - "layered non orthogonal random access with sic and transmit diversity for reliable transmissions"
    - "noma based random access with multichannel aloha"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "coded random access applying codes on graphs to design random access protocols"

Connected through hyperedge HE-3 (induced by source paper "a cross layer approach to data aided sensing using compressive random access"):
  with members:
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "internet of things iot a vision architectural elements and future directions"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "noma based compressive random access using gaussian spreading"):
  with members:
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "performance of hierarchical sparse detectors for massive mtc"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a variational inference based detection method for repetition coded generalized spatial modulation"
    - "layered non orthogonal random access with sic and transmit diversity for reliable transmissions"
    - "noma based random access with multichannel aloha"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

Connected through hyperedge HE-5 (induced by source paper "performance of hierarchical sparse detectors for massive mtc"):
  with members:
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "random pilot and data access in massive mimo for machine type communications"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "compressive random access using a common overloaded control channel"
    - "characterization of coded random access with compressive sensing based multi user detection"
    - "model based compressive sensing"

Connected through hyperedge HE-6 (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"
    - "neighbor discovery in a wireless sensor network multipacket reception capability and physical layer signal processing"
    - "neighbor discovery for wireless networks via compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "non orthogonal random access nora for 5g networks"):
  with members:
    - "a stochastic geometric analysis of device to device communications operating over generalized fading channels"
    - "cooperative non orthogonal multiple access with simultaneous wireless information and power transfer"
    - "what will 5g be"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

Connected through hyperedge HE-8 (induced by source paper "stable separation and super resolution of mixture models"):
  with members:
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"
    - "convexity in source separation models geometry and algorithms"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "near minimax line spectral estimation"
    - "compressive demodulation of mutually interfering signals"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"

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)