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 "smooth heaps and a dual view of self adjusting data structures"):
  Abstract: We present a new connection between self-adjusting binary search trees (BSTs) and heaps, two fundamental, extensively studied, and practically relevant families of data structures. Roughly speaking, we map an arbitrary heap algorithm within a broad and natural model, to a corresponding BST algorithm with the same cost on a dual sequence of operations (i.e. the same sequence with the roles of time and key-space switched). This is the first general transformation between the two families of data structures. #R##N#There is a rich theory of dynamic optimality for BSTs (i.e. the theory of competitiveness between BST algorithms). The lack of an analogous theory for heaps has been noted in the literature. Through our connection, we transfer all instance-specific lower bounds known for BSTs to a general model of heaps, initiating a theory of dynamic optimality for heaps. #R##N#On the algorithmic side, we obtain a new, simple and efficient heap algorithm, which we call the smooth heap. We show the smooth heap to be the heap-counterpart of Greedy, the BST algorithm with the strongest proven and conjectured properties from the literature, widely believed to be instance-optimal. Assuming the optimality of Greedy, the smooth heap is also optimal within our model of heap algorithms. #R##N#Intriguingly, the smooth heap, although derived from a non-practical BST algorithm, is simple and easy to implement (e.g. it stores no auxiliary data besides the keys and tree pointers). It can be seen as a variation on the popular pairing heap data structure, extending it with a "power-of-two-choices" type of heuristic. For the smooth heap we obtain instance-specific upper bounds, with applications in adaptive sorting, and we see it as a promising candidate for the long-standing question of a simpler alternative to Fibonacci heaps.

Includes members:
  Member "weighted dynamic finger in binary search trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "new paths from splay to dynamic optimality")
  Member "improved bounds for multipass pairing heaps and path balanced binary search trees":
    also connected through other hyperedges:
      (none)
  Member "pairing heaps the forward variant":
    also connected through other hyperedges:
      (none)
  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 "pattern avoiding access in binary search trees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "new paths from splay to dynamic optimality")
  Member "greedy is an almost optimal deque":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pinning down the strong wilber 1 bound for binary search trees")
  Member "a back to basics empirical study of priority queues":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal algorithms for ranked enumeration of answers to full conjunctive queries")
  Member "a tight lower bound for decrease key in the pure heap model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pairing heaps the forward variant")

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)