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: "lower bounds for predecessor searching in the cell probe model"
  Abstract: We consider a fundamental problem in data structures, static predecessor searching: Given a subset S of size n from the universe [m], store S so that queries of the form "What is the predecessor of x in S?" can be answered efficiently. We study this problem in the cell probe model introduced by Yao. Recently, Beame and Fich obtained optimal bounds on the number of probes needed by any deterministic query scheme if the associated storage scheme uses only n^{O(1)} cells of word size (\log m)^{O(1)} bits. We give a new lower bound proof for this problem that matches the bounds of Beame and Fich. Our lower bound proof has the following advantages: it works for randomised query schemes too, while Beame and Fich's proof works for deterministic query schemes only. It also extends to `quantum address-only' query schemes that we define in this paper, and is simpler than Beame and Fich's proof. We prove our lower bound using the round elimination approach of Miltersen, Nisan, Safra and Wigderson. Using tools from information theory, we prove a strong round elimination lemma for communication complexity that enables us to obtain a tight lower bound for the predecessor problem. Our strong round elimination lemma also extends to quantum communication complexity. We also use our round elimination lemma to obtain a rounds versus communication tradeoff for the `greater-than' problem, improving on the tradeoff in Miltersen et al. We believe that our round elimination lemma is of independent interest and should have other applications.

Connected through hyperedge HE-1 (induced by source paper "distributed and streaming linear programming in low dimensions"):
  with members:
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "communication steps for parallel query processing"
    - "efficient protocols for distributed classification and optimization"
    - "sorting searching and simulation in the mapreduce framework"
    - "lower bounds for number in hand multiparty communication complexity made easy"
    - "linear programming in the semi streaming model with application to the maximum matching problem"

Connected through hyperedge HE-2 (induced by source paper "communication complexity of cake cutting"):
  with members:
    - "consensus halving is ppa complete"
    - "the query complexity of cake cutting"
    - "combinatorial auctions do need modest interaction"
    - "a discrete and bounded envy free cake cutting protocol for any number of agents"
    - "waste makes haste bounded time algorithms for envy free cake cutting with free disposal"
    - "hardness results for consensus halving"
    - "communication complexity for algorithm designers"
    - "welfare maximization with limited interaction"

Connected through hyperedge HE-3 (induced by source paper "combinatorial auctions do need modest interaction"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "on simultaneous two player combinatorial auctions"
    - "best response dynamics in combinatorial auctions with item bidding"
    - "computational efficiency requires simple taxation"
    - "welfare maximization with limited interaction"
    - "combinatorial auctions via posted prices"
    - "economic efficiency requires interaction"
    - "simultaneous auctions are almost efficient"

Connected through hyperedge HE-4 (induced by source paper "dynamic integer sets with optimal rank select and predecessor search"):
  with members:
    - "more haste less waste lowering the redundancy in fully indexable dictionaries"
    - "dynamic ordered sets with exponential search trees"

Connected through hyperedge HE-5 (induced by source paper "the space complexity of recognizing well parenthesized expressions in the streaming model the index function revisited"):
  with members:
    - "streaming complexity of checking priority queues"
    - "lower bounds for sparse recovery"
    - "information cost tradeoffs for augmented index and streaming language recognition"
    - "recognizing well parenthesized expressions in the streaming model"
    - "one way communication complexity and the neciporuk lower bound on formula size"

Connected through hyperedge HE-6 (induced by source paper "unifying the landscape of cell probe lower bounds"):
  with members:
    - "logarithmic lower bounds in the cell probe model"

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)