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 "push down trees optimal self adjusting complete trees"):
  Abstract: Since Sleator and Tarjan's seminal work on self-adjusting lists, heaps and binary search trees, researchers have been fascinated by dynamic datastructures and the questions related to their performance over time. This paper initiates the study of another classic datastructure, self-adjusting (binary) Complete Trees (CTs): trees which do not provide a simple search mechanism but allow to efficiently access items given a global map. Our problem finds applications, e.g., in the context of warehouse optimization or self-adjusting communication networks which can adapt to the demand they serve. We show that self-adjusting complete trees assume an interesting position between the complexity of self-adjusting (unordered) lists and binary search trees. In particular, we observe that in contrast to lists, a simple move-to-front strategy alone is insufficient to achieve a constant competitive ratio. Rather, and similarly to binary search trees, an additional (efficient) tree update rule is needed. Intriguingly, while it is unknown whether the working set is a lower bound for binary search trees, we show that this holds in our model. So while finding an update rule is still an open problem for binary search trees, this paper shows that there exists a simple, random update rule for complete trees. Our main result is a dynamically optimal (i.e., constant competitive) self-adjusting CT called Push-Down Tree, on expectation against an oblivious adversary. At the heart of our approach lies a distributed algorithm called Random-Push: this algorithm approximates a natural notion of Most Recently Used (MRU) tree (essentially an approximate working set), by first performing move-to-front, but then pushing less recently accessed items down the tree using a random walk.

Includes members:
  Member "demand aware network designs of bounded degree":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scheduling for weighted flow and completion times in reconfigurable networks")
  Member "the landscape of bounds for binary search trees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "new paths from splay to dynamic optimality")
  Member "an o loglog n competitive binary search tree with optimal worst case access times":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "de amortizing binary search 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.DS(Data Structures and Algorithms)