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 "some problems in compact message passing"):
  Abstract: This paper seeks to address the question of designing distributed algorithms for the setting of compact memory i.e. sublinear bits working memory for arbitrary connected networks. The nodes in our networks may have much lower internal memory as compared to the number of their possible neighbours implying that a node may not be able to store all the IDs of its neighbours. These algorithms are useful for large networks of small devices such as the Internet of Things, for wireless or ad-hoc networks, and, in general, as memory efficient algorithms. We introduce the Compact Message Passing(CMP) model;an extension of the standard message passing model considered at a finer granularity where a node can interleave reads and writes with internal computations, using a port only once in a round. The interleaving is required for meaningful computations due to the low memory requirement and is akin to a distributed network with nodes executing streaming algorithms. Note that the internal memory size upper bounds the message sizes and hence e.g. for log-memory, the model is weaker than the Congest model; for such models our algorithms will work directly too. We present early results in the CMP model for nodes with log^2-memory. We introduce the concepts of local compact functions and compact protocols and give solutions for some classic distributed problems (leader election, tree constructions and traversals). We build on these to solve the open problem of compact preprocessing for the compact self-healing routing algorithm CompactFTZ posed in Compact Routing Messages in Self-Healing Trees(TCS2017) by designing local compact functions for finding particular subtrees of labeled binary trees. Hence, we introduce the first fully compact self-healing routing algorithm. We also give independent fully compact versions of the Forgiving Tree[PODC08] and Thorup-Zwick's tree based compact routing[SPAA01].

Includes members:
  Member "linear size hopsets with small hopbound and distributed routing with low memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "a self stabilizing general de bruijn graph":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on underlay aware self stabilizing overlay networks")
  Member "the computational power of beeps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed voting in beep model")
  Member "fast partial distance estimation and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast approximate shortest paths in the congested clique")
  Member "dex self healing expanders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relays a new approach for the finite departure problem in overlay networks")
  Member "algorithms for self healing networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compact routing messages in self healing trees")
  Member "the forgiving graph a distributed data structure for low stretch under adversarial attack":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relays a new approach for the finite departure problem in overlay networks")
  Member "the forgiving tree a self healing distributed data structure":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compact routing messages in self healing trees")

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.DC(Distributed, Parallel, and Cluster Computing)