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: "many access for small packets based on precoding and sparsity aware recovery"
  Abstract: Modern mobile terminals produce massive small data packets. For these short-length packets, it is inefficient to follow the current multiple access schemes to allocate transmission resources due to heavy signaling overhead. We propose a non-orthogonal many-access scheme that is well suited for the future communication systems equipped with many receive antennas. The system is modeled as having a block-sparsity pattern with unknown sparsity level (i.e., unknown number of transmitted messages). Block precoding is employed at each single-antenna transmitter to enable the simultaneous transmissions of many users. The number of simultaneously served active users is allowed to be even more than the number of receive antennas. Sparsity-aware recovery is designed at the receiver for joint user detection and symbol demodulation. To reduce the effects of channel fading on signal recovery, normalized block orthogonal matching pursuit (BOMP) algorithm is introduced, and based on its approximate performance analysis, we develop interference cancellation based BOMP (ICBOMP) algorithm. The ICBOMP performs error correction and detection in each iteration of the normalized BOMP. Simulation results demonstrate the effectiveness of the proposed scheme in small packet services, as well as the advantages of ICBOMP in improving signal recovery accuracy and reducing computational cost.

Connected through hyperedge HE-1 (induced by source paper "sparse vector coding for ultra reliable and low latency communications"):
  with members:
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "on or many access channels"
    - "capacity of gaussian many access channels"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "generalized orthogonal matching pursuit"

Connected through hyperedge HE-2 (induced by source paper "joint domain based massive access for small packets traffic of uplink wireless channel"):
  with members:
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

Connected through hyperedge HE-3 (induced by source paper "capacity of gaussian many access channels"):
  with members:
    - "on or many access channels"
    - "sparse ofdm a compressive sensing approach to asynchronous neighbor discovery"
    - "identifying randomly activated users via sign compute resolve on graphs"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "many access channels the gaussian case with random user activities"
    - "many broadcast channels definition and capacity in the degraded case"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "error performance of channel coding in random access communication"

Connected through hyperedge HE-4 (induced by source paper "stopping condition for greedy block sparse signal recovery"):
  with members:
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse signal reconstruction via iterative support detection"

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)