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 "rate selection and power adaptation using maximal ratio combining for the random access gaussian channel"):
  Abstract: With the emergence of machine-driven communi- cation, there is a renewed interest in the design of random multiple access schemes for networks with large number of active devices. Many of the recently proposed access paradigms are enhancements to slotted ALOHA. One of the popular schemes, irregular repetition slotted ALOHA (IRSA), is based on an analogy between multiple access with successive interference cancellation and message-passing decoding on bipartite graphs. Most of the results on IRSA and its variants focus on the collision channel and they ignore physical limitations such as transmit power constraints and additive Gaussian noise at the physical layer. As such, naive extensions of IRSA to the Gaussian multiple access channel are not power efficient in the low signal-to-noise- ratio regime. This work introduces a novel paradigm whereby devices adapt their rates and/or transmit powers based on their chosen repetition profiles. The receiver performs maximal ratio combining over all the replicas prior to decoding a message. Numerical results for finite number of users show that the proposed scheme can provide substantial improvements in terms of power efficiency and average rate over standard IRSA.

Includes members:
  Member "enhancing contention resolution aloha using combining techniques":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "protograph ldpc code design for asynchronous random access")
  Member "coded random access applying codes on graphs to design random access protocols":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo")
  Member "high throughput random access via codes on graphs coded slotted aloha":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "random access for massive machine type communications")

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)