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: "time space trade offs for predecessor search"
  Abstract: We develop a new technique for proving cell-probe lower bounds for static data structures. Previous lower bounds used a reduction to communication games, which was known not to be tight by counting arguments. We give the first lower bound for an explicit problem which breaks this communication complexity barrier. In addition, our bounds give the first separation between polynomial and near linear space. Such a separation is inherently impossible by communication complexity. #R##N#Using our lower bound technique and new upper bound constructions, we obtain tight bounds for searching predecessors among a static set of integers. Given a set Y of n integers of l bits each, the goal is to efficiently find predecessor(x) = max{y in Y | y <= x}, by representing Y on a RAM using space S. #R##N#In external memory, it follows that the optimal strategy is to use either standard B-trees, or a RAM algorithm ignoring the larger block size. In the important case of l = c*lg n, for c>1 (i.e. polynomial universes), and near linear space (such as S = n*poly(lg n)), the optimal search time is Theta(lg l). Thus, our lower bound implies the surprising conclusion that van Emde Boas' classic data structure from [FOCS'75] is optimal in this case. Note that for space n^{1+eps}, a running time of O(lg l / lglg l) was given by Beame and Fich [STOC'99].

Connected through hyperedge HE-1 (induced by source paper "efficient and compact representations of prefix codes"):
  with members:
    - "new lower and upper bounds for representing sequences"
    - "on compressing permutations and adaptive sorting"
    - "fixed block compression boosting in fm indexes"
    - "compressed representations of permutations and applications"
    - "fast and compact prefix codes"
    - "worst case optimal adaptive prefix coding"
    - "dynamic asymmetric communication"
    - "large alphabets and incompressibility"

Connected through hyperedge HE-2 (induced by source paper "optimal encodings for range majority queries"):
  with members:
    - "better space bounds for parameterized range majority and minority"
    - "top k color queries for document retrieval"
    - "searching for frequent colors in rectangles"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "practical entropy compressed rank select dictionary"

Connected through hyperedge HE-3 (induced by source paper "new lower and upper bounds for representing sequences"):
  with members:
    - "optimal trade off for succinct string indexes"
    - "efficient fully compressed sequence representations"
    - "compressed representations of permutations and applications"
    - "fast in memory xpath search over compressed text and tree indexes"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "large alphabets and incompressibility"
    - "practical entropy compressed rank select dictionary"

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)