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 "query to communication lifting for bpp"):
  Abstract: For any $n$-bit boolean function $f$, we show that the randomized communication complexity of the composed function $f\circ g^n$, where $g$ is an index gadget, is characterized by the randomized decision tree complexity of $f$. In particular, this means that many query complexity separations involving randomized models (e.g., classical vs. quantum) automatically imply analogous separations in communication complexity.

Includes members:
  Member "entangled simultaneity versus classical interactivity in communication complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bare quantum simultaneity versus classical interactivity in communication complexity")
  Member "randomized query complexity of sabotaged and composed functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a tight composition theorem for the randomized query complexity of partial functions")
  Member "nearly optimal separations between communication or query complexity and partitions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal separation and strong direct sum for randomized query complexity")
  Member "approximating rectangles by juntas and weakly exponential lower bounds for lp relaxations of csps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "list decodable subspace recovery")
  Member "communication complexity of approximate nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "separations in query complexity based on pointer functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a tight composition theorem for the randomized query complexity of partial functions")
  Member "lower bounds on the size of semidefinite programming relaxations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lifting with simple gadgets and applications to circuit and proof complexity")
  Member "approximate constraint satisfaction requires large lp relaxations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "new dependencies of hierarchies in polynomial optimization")

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)