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 "polynomial pass lower bounds for graph streaming algorithms"):
  Abstract: We present new lower bounds that show that a polynomial number of passes are necessary for solving some fundamental graph problems in the streaming model of computation. For instance, we show that any streaming algorithm that finds a weighted minimum $s$-$t$ cut in an $n$-vertex undirected graph requires $n^{2-o(1)}$ space unless it makes $n^{\Omega(1)}$ passes over the stream. #R##N#To prove our lower bounds, we introduce and analyze a new four-player communication problem that we refer to as the hidden-pointer chasing problem. This is a problem in spirit of the standard pointer chasing problem with the key difference that the pointers in this problem are hidden to players and finding each one of them requires solving another communication problem, namely the set intersection problem. Our lower bounds for graph problems are then obtained by reductions from the hidden-pointer chasing problem. #R##N#Our hidden-pointer chasing problem appears flexible enough to find other applications and is therefore interesting in its own right. To showcase this, we further present an interesting application of this problem beyond streaming algorithms. Using a reduction from hidden-pointer chasing, we prove that any algorithm for submodular function minimization needs to make $n^{2-o(1)}$ value queries to the function unless it has a polynomial degree of adaptivity.

Includes members:
  Member "smaller cuts higher lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "brief announcement does preprocessing help under congestion")
  Member "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interaction is necessary for distributed learning with privacy or communication constraints")
  Member "an exponential speedup in parallel running time for submodular maximization without loss in approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "non monotone submodular maximization with nearly optimal adaptivity complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interlaced greedy algorithm for maximization of submodular functions in nearly linear time")
  Member "submodular maximization with optimal approximation adaptivity and query complexity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "tight bounds on the round complexity of the distributed maximum coverage problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "non monotone submodular maximization in exponentially fewer iterations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")

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)