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 complexity of inevitable intersection"):
  Abstract: Most or all known methods for analysing communication complexity of unstructured search are based on the hardness of the set disjointness problem. We feel that such techniques may "hide" certain important aspects of the original problem. Intuitively, search is a much simpler task: while set disjointness is hard even for NP, a successful search inherently results in a short witness, which makes it easy for NP. The possibility to deduce hardness of search problems from that of disjointness may be just an artefact of the definitions. #R##N#We construct a natural variation of the intersection-search problem, where the input comes from a product distribution, and nevertheless, every pair of input subsets share at least one element. We call this problem inevitable intersection, its analysis seems to require a new, more subtle approach $-$ in particular, not relying on the hardness of set disjointness. #R##N#To prove a lower bound on the communication complexity of inevitable intersection, we identify such properties of large rectangles that make it impossible to partition the input matrix into a small number of "nearly-monochromatic" rectangles (the analysis is "non-local": it addresses not the existence of a large rectangle of low discrepancy, but the possibility to partition the whole matrix into such rectangles). We believe that this technique provides new insight into the combinatorial structure of "search-like" communication problems.

Includes members:
  Member "on the communication complexity of sparse set disjointness and exists equal problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the communication complexity of set intersection and multiple equality testing")
  Member "an optimal lower bound on the communication complexity of gap hamming distance":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "the partition bound for classical communication complexity and query complexity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "query to communication lifting using low discrepancy gadgets")
  Member "a strong direct product theorem for disjointness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "query to communication lifting using low discrepancy gadgets")

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)