human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "separations in query complexity based on pointer functions"
  Abstract: In 1986, Saks and Wigderson conjectured that the largest separation between deterministic and zero-error randomized query complexity for a total boolean function is given by the function $f$ on $n=2^k$ bits defined by a complete binary tree of NAND gates of depth $k$, which achieves $R_0(f) = O(D(f)^{0.7537\ldots})$. We show this is false by giving an example of a total boolean function $f$ on $n$ bits whose deterministic query complexity is $\Omega(n/\log(n))$ while its zero-error randomized query complexity is $\tilde O(\sqrt{n})$. We further show that the quantum query complexity of the same function is $\tilde O(n^{1/4})$, giving the first example of a total function with a super-quadratic gap between its quantum and deterministic query complexities. #R##N#We also construct a total boolean function $g$ on $n$ variables that has zero-error randomized query complexity $\Omega(n/\log(n))$ and bounded-error randomized query complexity $R(g) = \tilde O(\sqrt{n})$. This is the first super-linear separation between these two complexity measures. The exact quantum query complexity of the same function is $Q_E(g) = \tilde O(\sqrt{n})$. #R##N#These two functions show that the relations $D(f) = O(R_1(f)^2)$ and $R_0(f) = \tilde O(R(f)^2)$ are optimal, up to poly-logarithmic factors. Further variations of these functions give additional separations between other query complexity measures: a cubic separation between $Q$ and $R_0$, a $3/2$-power separation between $Q_E$ and $R$, and a 4th power separation between approximate degree and bounded-error randomized query complexity. #R##N#All of these examples are variants of a function recently introduced by \goos, Pitassi, and Watson which they used to separate the unambiguous 1-certificate complexity from deterministic query complexity and to resolve the famous Clique versus Independent Set problem in communication complexity.

Connected through hyperedge HE-1 (induced by source paper "a tight composition theorem for the randomized query complexity of partial functions"):
  with members:
    - "the power of many samples in query complexity"
    - "a new minimax theorem for randomized algorithms"
    - "optimal separation and strong direct sum for randomized query complexity"
    - "a composition theorem for randomized query complexity via max conflict complexity"
    - "a composition theorem for randomized query complexity"
    - "randomized query complexity of sabotaged and composed functions"
    - "composition limits and separating examples for some boolean function complexity measures"
    - "a composition theorem for decision tree complexity"

Connected through hyperedge HE-2 (induced by source paper "a nearly optimal lower bound on the approximate degree of ac 0"):
  with members:
    - "on szk and pp"
    - "dual polynomials for collision and element distinctness"
    - "distribution independent reliable learning"
    - "dual lower bounds for approximate degree and markov bernstein inequalities"
    - "faster private release of marginals on small databases"
    - "hardness amplification and the approximate degree of constant depth circuits"
    - "faster algorithms for privately releasing marginals"
    - "a separation of np and conp in multiparty communication complexity"

Connected through hyperedge HE-3 (induced by source paper "query to communication lifting for bpp"):
  with members:
    - "entangled simultaneity versus classical interactivity in communication complexity"
    - "randomized query complexity of sabotaged and composed functions"
    - "nearly optimal separations between communication or query complexity and partitions"
    - "approximating rectangles by juntas and weakly exponential lower bounds for lp relaxations of csps"
    - "communication complexity of approximate nash equilibria"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "approximate constraint satisfaction requires large lp relaxations"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

Connected through hyperedge HE-4 (induced by source paper "low sensitivity functions from unambiguous certificates"):
  with members:
    - "randomized query complexity of sabotaged and composed functions"
    - "nearly optimal separations between communication or query complexity and partitions"
    - "degree and sensitivity tails of two distributions"
    - "smooth boolean functions are easy efficient algorithms for low sensitivity functions"
    - "sensitivity versus certificate complexity of boolean functions"
    - "size of sets with small sensitivity a generalization of simon s lemma"
    - "an o n 0 4732 upper bound on the complexity of the gks communication game"
    - "separating decision tree complexity from subcube partition complexity"

Connected through hyperedge HE-5 (induced by source paper "efficient quantum algorithms for gapped group testing and junta testing"):
  with members:
    - "a super grover separation between randomized and quantum query complexities"
    - "a note on the quantum collision and set equality problems"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

Question: Please tell me which class the center node 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)