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 "equal subset sum faster than the meet in the middle"):
  Abstract: In the Equal-Subset-Sum problem, we are given a set $S$ of $n$ integers and the problem is to decide if there exist two disjoint nonempty subsets $A,B \subseteq S$, whose elements sum up to the same value. The problem is NP-complete. The state-of-the-art algorithm runs in $O^{*}(3^{n/2}) \le O^{*}(1.7321^n)$ time and is based on the meet-in-the-middle technique. In this paper, we improve upon this algorithm and give $O^{*}(1.7088^n)$ worst case Monte Carlo algorithm. This answers the open problem from Woeginger's inspirational survey. #R##N#Additionally, we analyse the polynomial space algorithm for Equal-Subset-Sum. A naive polynomial space algorithm for Equal-Subset-Sum runs in $O^{*}(3^n)$ time. With read-only access to the exponentially many random bits, we show a randomized algorithm running in $O^{*}(2.6817^n)$ time and polynomial space.

Includes members:
  Member "ppp completeness with connections to cryptography":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the classes ppa k existence from arguments modulo k")
  Member "a simple near linear pseudopolynomial time randomized algorithm for subset sum":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scheduling lower bounds via and subset sum")
  Member "subset sum made simple":
    also connected through other hyperedges:
      (none)
  Member "seth based lower bounds for subset sum and bicriteria path":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scheduling lower bounds via and subset sum")
  Member "on problems as hard as cnf sat":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "a near linear pseudopolynomial time algorithm for subset sum":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scheduling lower bounds via and subset sum")
  Member "number balancing is as hard as minkowski s theorem and shortest vector":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "balancing gaussian vectors in high dimension")
  Member "a faster pseudopolynomial time algorithm for subset sum":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scheduling lower bounds via and subset sum")

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)