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: "improved channel estimation for interference cancellation in random access methods for satellite communications"
  Abstract: In the context of satellite communications, random access (RA) methods can significantly increase throughput and reduce latency over the network. The recent RA methods are based on multi-user multiple access transmission at the same time and frequency combined with interference cancellation and iterative decoding at the receiver. Generally, it is assumed that perfect knowledge of the interference is available at the receiver. In practice, the interference term has to be accurately estimated to avoid performance degradation. Several estimation techniques have been proposed lately in the case of superimposed signals. In this paper, we present an improved channel estimation technique that combines estimation using an autocorrelation based method and the Expectation-Maximization algorithm, and uses Pilot Symbol Assisted Modulation to further improve the performance and achieve optimal interference cancellation.

Connected through hyperedge HE-1 (induced by source paper "estimation of timing offsets and phase shifts between packet replicas in marsala random access"):
  with members:
    - "an enhanced multiple random access scheme for satellite communications"

Connected through hyperedge HE-2 (induced by source paper "performance evaluation of marsala with synchronisation errors in satellite communications"):
  with members:
    - "an enhanced multiple random access scheme for satellite communications"

Connected through hyperedge HE-3 (induced by source paper "seek and decode random access with physical layer network coding and multiuser detection"):
  with members:
    - "on the subtleties of q pam linear physical layer network coding"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "coded random access applying codes on graphs to design random access protocols"
    - "characterization of coded random access with compressive sensing based multi user detection"
    - "network coded multiple access"
    - "random access with physical layer network coding"
    - "reliable physical layer network coding"

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)