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: "computing heat kernel pagerank and a local clustering algorithm"
  Abstract: Heat kernel pagerank is a variation of Personalized PageRank given in an exponential formulation. In this work, we present a sublinear time algorithm for approximating the heat kernel pagerank of a graph. The algorithm works by simulating random walks of bounded length and runs in time $O\big(\frac{\log(\epsilon^{-1})\log n}{\epsilon^3\log\log(\epsilon^{-1})}\big)$, assuming performing a random walk step and sampling from a distribution with bounded support take constant time. #R##N#The quantitative ranking of vertices obtained with heat kernel pagerank can be used for local clustering algorithms. We present an efficient local clustering algorithm that finds cuts by performing a sweep over a heat kernel pagerank vector, using the heat kernel pagerank approximation algorithm as a subroutine. Specifically, we show that for a subset $S$ of Cheeger ratio $\phi$, many vertices in $S$ may serve as seeds for a heat kernel pagerank vector which will find a cut of conductance $O(\sqrt{\phi})$.

Connected through hyperedge HE-1 (induced by source paper "compressive sensing for cut improvement and local clustering"):
  with members:
    - "statistical guarantees for local graph clustering"
    - "power weighted shortest paths for clustering euclidean data"
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "an optimization approach to locally biased graph algorithms"
    - "a simple and strongly local flow based method for cut improvement"
    - "detecting overlapping communities from local spectral subspaces"
    - "uncovering the small community structure in large networks a local spectral approach"

Connected through hyperedge HE-2 (induced by source paper "efficient estimation of heat kernel pagerank for local clustering"):
  with members:
    - "capacity releasing diffusion for speed and locality"
    - "parallel local graph clustering"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a simple and strongly local flow based method for cut improvement"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "fast bidirectional probability estimation in markov models"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"

Connected through hyperedge HE-3 (induced by source paper "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"):
  with members:
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "routing under balance"
    - "computing maximum flow with augmenting electrical flows"
    - "on fully dynamic graph sparsifiers"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "an efficient parallel algorithm for spectral sparsification of laplacian and sddm matrix polynomials"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "constructing linear sized spectral sparsification in almost linear time"

Connected through hyperedge HE-4 (induced by source paper "parallel local graph clustering"):
  with members:
    - "a simple and strongly local flow based method for cut improvement"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (induced by source paper "an optimization approach to locally biased graph algorithms"):
  with members:
    - "a simple and strongly local flow based method for cut improvement"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"
    - "semi supervised eigenvectors for large scale locally biased learning"
    - "social structure of facebook networks"
    - "the anatomy of the facebook social graph"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-6 (induced by source paper "predicting risky behavior in social communities"):
  with members:
    - "community detection in networks with node attributes"
    - "friendship networks and social status"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "distributed algorithms for finding local clusters using heat kernel pagerank"):
  with members:
    - "fast distributed pagerank computation"
    - "heat kernel based community detection"
    - "distributed computation of large scale graph problems"
    - "distributed random walks"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"
    - "graphlab a new framework for parallel machine learning"
    - "finding sparse cuts locally using evolving sets"

Connected through hyperedge HE-8 (induced by source paper "solving local linear systems with boundary conditions using heat kernel pagerank"):
  with members:
    - "an efficient parallel solver for sdd linear systems"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "matrix inversion is as easy as exponentiation"
    - "recovering low rank matrices from few coefficients in any basis"
    - "near linear work parallel sdd solvers low diameter decomposition and low stretch subgraphs"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

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.DS(Data Structures and Algorithms)