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 "sign compute resolve for tree splitting random access"):
  Abstract: We present a framework for random access that is based on three elements: physical-layer network coding (PLNC), signature codes and tree splitting. In presence of a collision, physical-layer network coding enables the receiver to decode, i.e. compute, the sum of the packets that were transmitted by the individual users. For each user, the packet consists of the user's signature, as well as the data that the user wants to communicate. As long as no more than K users collide, their identities can be recovered from the sum of their signatures. This framework for creating and transmitting packets can be used as a fundamental building block in random access algorithms, since it helps to deal efficiently with the uncertainty of the set of contending terminals. In this paper we show how to apply the framework in conjunction with a tree-splitting algorithm, which is required to deal with the case that more than K users collide. We demonstrate that our approach achieves throughput that tends to 1 rapidly as K increases. We also present results on net data-rate of the system, showing the impact of the overheads of the constituent elements of the proposed protocol. We compare the performance of our scheme with an upper bound that is obtained under the assumption that the active users are a priori known. Also, we consider an upper bound on the net data-rate for any PLNC based strategy in which one linear equation per slot is decoded. We show that already at modest packet lengths, the net data-rate of our scheme becomes close to the second upper bound, i.e. the overhead of the contention resolution algorithm and the signature codes vanishes.

Includes members:
  Member "identifying randomly activated users via sign compute resolve on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity of gaussian many access channels")
  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 "many access channels the gaussian case with random user activities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "sign compute resolve for random access":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural network aided decoding for physical layer network coding random access")
  Member "coded splitting tree protocols":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a random access protocol for pilot allocation in crowded massive mimo systems")
  Member "random access with physical layer network coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random access for massive machine type communications")
  Member "reliable physical layer network coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards massive connectivity support for scalable mmtc communications in 5g networks")
  Member "physical layer network coding tutorial survey and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)