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 "communication lower bounds using dual polynomials"):
  Abstract: Representations of Boolean functions by real polynomials play an important role in complexity theory. Typically, one is interested in the least degree of a polynomial p(x_1,...,x_n) that approximates or sign-represents a given Boolean function f(x_1,...,x_n). This article surveys a new and growing body of work in communication complexity that centers around the dual objects, i.e., polynomials that certify the difficulty of approximating or sign-representing a given function. We provide a unified guide to the following results, complete with all the key proofs: #R##N#(1) Sherstov's Degree/Discrepancy Theorem, which translates lower bounds on the threshold degree of a Boolean function into upper bounds on the discrepancy of a related function; #R##N#(2) Two different methods for proving lower bounds on bounded-error communication based on the approximate degree: Sherstov's pattern matrix method and Shi and Zhu's block composition method; #R##N#(3) Extension of the pattern matrix method to the multiparty model, obtained by Lee and Shraibman and by Chattopadhyay and Ada, and the resulting improved lower bounds for DISJOINTNESS; #R##N#(4) David and Pitassi's separation of NP and BPP in multiparty communication complexity for k=(1-eps)log n players.

Includes members:
  Member "multiparty communication complexity of disjointness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a nearly optimal lower bound on the approximate degree of ac 0")
  Member "separating nof communication complexity classes rp and np":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a nearly optimal lower bound on the approximate degree of ac 0")
  Member "disjointness is hard in the multi party number on the forehead model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hardness amplification and the approximate degree of constant depth circuits")
  Member "communication complexity lower bounds by polynomials":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sensitivity affine transforms and quantum communication complexity")
  Member "bounds for small error and zero error quantum algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an investigation on support vector clustering for big data in quantum paradigm")

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.CC(Computational Complexity)