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: "tribes is hard in the message passing model"
  Abstract: We consider the point-to-point message passing model of communication in which there are $k$ processors with individual private inputs, each n-bit long. Each processor is located at the node of an underlying undirected graph and has access to private random coins. An edge of the graph is a private channel of communication between its endpoints. The processors have to compute a given function of all their inputs by communicating along these channels. While this model has been widely used in distributed computing, strong lower bounds on the amount of communication needed to compute simple functions have just begun to appear.#R##N##R##N#In this work, we prove a tight lower bound of \Omega(kn) on the communication needed for computing the Tribes function, when the  underlying graph is a star of k+1 nodes that has k leaves with inputs and a center with no input. A lower bound on this topology easily implies comparable bounds for others. Our lower bounds are obtained by building upon the recent information theoretic techniques of Braverman et al. ([4], FOCS'13) and combining it with the earlier work of Jayram, Kumar and Sivakumar ([10], STOC'03). This approach yields information complexity bounds that are of independent interest.

Connected through hyperedge HE-1 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms"):
  with members:
    - "smaller cuts higher lower bounds"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

Connected through hyperedge HE-2 (induced by source paper "multi party protocols information complexity and privacy"):
  with members:
    - "the range of topological effects on communication"
    - "tight bounds for set disjointness in the message passing model"
    - "information equals amortized communication"
    - "lower bounds for number in hand multiparty communication complexity made easy"
    - "distributed verification and hardness of distributed approximation"
    - "a strong direct product theorem for disjointness"
    - "a direct sum theorem in communication complexity via message compression"

Connected through hyperedge HE-3 (induced by source paper "tight network topology dependent bounds on rounds of communication"):
  with members:
    - "distributed computation of large scale graph problems"
    - "when distributed computation is communication expensive"
    - "tight bounds for distributed functional monitoring"
    - "distributed verification and hardness of distributed approximation"
    - "bicriteria network design problems"

Connected through hyperedge HE-4 (induced by source paper "the range of topological effects on communication"):
  with members:
    - "on the communication complexity of linear algebraic problems in the message passing model"
    - "communication steps for parallel query processing"
    - "when distributed computation is communication expensive"
    - "tight bounds for set disjointness in the message passing model"
    - "optimal deterministic routing and sorting on the congested clique"
    - "distributed learning communication complexity and privacy"
    - "lower bounds for number in hand multiparty communication complexity made easy"
    - "tight bounds for distributed functional monitoring"

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)