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: "on the communication complexity of linear algebraic problems in the message passing model"
  Abstract: We study the communication complexity of linear algebraic problems over finite fields in the multi-player message passing model, proving a number of tight lower bounds. Specifically, for a matrix which is distributed among a number of players, we consider the problem of determining its rank, of computing entries in its inverse, and of solving linear equations. We also consider related problems such as computing the generalized inner product of vectors held on different servers. We give a general framework for reducing these multi-player problems to their two-player counterparts, showing that the randomized $s$-player communication complexity of these problems is at least $s$ times the randomized two-player communication complexity. Provided the problem has a certain amount of algebraic symmetry, which we formally define, we can show the hardest input distribution is a symmetric distribution, and therefore apply a recent multi-player lower bound technique of Phillips et al. Further, we give new two-player lower bounds for a number of these problems. In particular, our optimal lower bound for the two-player version of the matrix rank problem resolves an open question of Sun and Wang. #R##N#A common feature of our lower bounds is that they apply even to the special "threshold promise" versions of these problems, wherein the underlying quantity, e.g., rank, is promised to be one of just two values, one on each side of some critical threshold. These kinds of promise problems are commonplace in the literature on data streaming as sources of hardness for reductions giving space lower bounds.

Connected through hyperedge HE-1 (induced by source paper "tight bounds for the subspace sketch problem with applications"):
  with members:
    - "distributed statistical estimation of matrix products with applications"
    - "high probability frequency moment sketches"
    - "optimal lower bounds for sketching graph cuts"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "on sketching quadratic forms"
    - "sketching as a tool for numerical linear algebra"
    - "quantile regression for large scale applications"
    - "the fast cauchy transform and faster robust linear regression"

Connected through hyperedge HE-2 (induced by source paper "space lower bounds for linear prediction"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "testing matrix rank optimally"
    - "extractor based time space lower bounds for learning"
    - "on communication complexity of classification problems"
    - "matrix norms in data streams faster multi pass and row order"
    - "optimal approximate matrix product in terms of stable rank"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"

Connected through hyperedge HE-3 (induced by source paper "space lower bounds for linear prediction in the streaming model"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "learning without interaction requires separation"
    - "detecting correlations with little memory and communication"
    - "testing matrix rank optimally"
    - "extractor based time space lower bounds for learning"
    - "time space tradeoffs for learning from small test spaces learning low degree polynomial functions"
    - "fast learning requires good memory a time space lower bound for parity learning"

Connected through hyperedge HE-4 (induced by source paper "on the multiparty communication complexity of testing triangle freeness"):
  with members:
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "distributed testing of excluded subgraphs"
    - "a hybrid sampling scheme for triangle counting"
    - "fast distributed algorithms for testing graph properties"
    - "dynamic graph stream algorithms in o n space"
    - "approximately counting triangles in sublinear time"
    - "algebraic methods in the congested clique"
    - "optimal principal component analysis in distributed and streaming models"

Connected through hyperedge HE-5 (induced by source paper "on estimating maximum matching size in graph streams"):
  with members:
    - "on approximating functions of the singular values in a stream"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "better bounds for matchings in the streaming model"
    - "superlinear lower bounds for multipass graph processing"
    - "maximum matching in semi streaming with few passes"
    - "improved approximation guarantees for weighted matching in the semi streaming model"

Connected through hyperedge HE-6 (induced by source paper "the range of topological effects on communication"):
  with members:
    - "tribes is hard 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"

Connected through hyperedge HE-7 (induced by source paper "distributed estimation of generalized matrix rank efficient algorithms and lower bounds"):
  with members:
    - "principal component analysis and higher correlations for distributed data"
    - "efficient estimation of eigenvalue counts in an interval"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "randomized algorithms for matrices and data"
    - "density matrix based algorithm for solving eigenvalue problems"
    - "robust principal component analysis"

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)