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: "faster algorithms for edge connectivity via random 2 out contractions"
  Abstract: We provide a simple new randomized contraction approach to the global minimum cut problem for simple undirected graphs. The contractions exploit 2-out edge sampling from each vertex rather than the standard uniform edge sampling. We demonstrate the power of our new approach by obtaining better algorithms for sequential, distributed, and parallel models of computation. Our end results include the following randomized algorithms for computing edge connectivity with high probability: #R##N#-- Two sequential algorithms with complexities $O(m \log n)$ and $O(m+n \log^3 n)$. These improve on a long line of developments including a celebrated $O(m \log^3 n)$ algorithm of Karger [STOC'96] and the state of the art $O(m \log^2 n (\log\log n)^2)$ algorithm of Henzinger et al. [SODA'17]. Moreover, our $O(m+n \log^3 n)$ algorithm is optimal whenever $m = \Omega(n \log^3 n)$. Within our new time bounds, whp, we can also construct the cactus representation of all minimal cuts. #R##N#-- An $O(n^{0.8} D^{0.2} + n^{0.9})$ round distributed algorithm, where D denotes the graph diameter. This improves substantially on a recent breakthrough of Daga et al. [STOC'19], which achieved a round complexity of $O(n^{1-1/353}D^{1/353} + n^{1-1/706})$, hence providing the first sublinear distributed algorithm for exactly computing the edge connectivity. #R##N#-- The first $O(1)$ round algorithm for the massively parallel computation setting with linear memory per machine.

Connected through hyperedge HE-1 (induced by source paper "parallel algorithms for small subgraph counting"):
  with members:
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "linear time subgraph counting graph degeneracy and the chasm at size six"
    - "exponentially faster massively parallel maximal matching"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"

Connected through hyperedge HE-2 (induced by source paper "massively parallel algorithms for distance approximation and spanners"):
  with members:
    - "walking randomly massively and efficiently"
    - "dynamic algorithms for the massively parallel computation model"
    - "mapreduce meets fine grained complexity mapreduce algorithms for apsp matrix multiplication 3 sum and beyond"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "parallel approximate undirected shortest paths via low hop emulators"
    - "faster parallel algorithm for approximate shortest path"
    - "exponentially faster massively parallel maximal matching"
    - "congested clique algorithms for graph spanners"

Connected through hyperedge HE-3 (induced by source paper "finding all global minimum cuts in practice"):
  with members:
    - "engineering kernelization for maximum cut"
    - "shared memory branch and reduce for multiterminal cuts"
    - "wegotyoucovered the winning solver from the pace 2019 implementation challenge vertex cover track"
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "experimental evaluation of parameterized algorithms for feedback vertex set"
    - "practical access to dynamic programming on tree decompositions"
    - "practical minimum cut algorithms"
    - "shared memory exact minimum cuts"

Connected through hyperedge HE-4 (induced by source paper "work efficient batch incremental minimum spanning trees with applications to the sliding window model"):
  with members:
    - "batch dynamic algorithms via parallel change propagation and applications to dynamic trees"
    - "parallel batch dynamic graph connectivity"
    - "improved worst case deterministic parallel dynamic minimum spanning forest"
    - "parallel minimum cuts in near linear work and low depth"
    - "faster fully dynamic minimum spanning forest"
    - "single pass sparsification in the streaming model with edge deletions"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-5 (induced by source paper "a simple algorithm for minimum cuts in near linear time"):
  with members:
    - "distributed edge connectivity in sublinear time"
    - "practical minimum cut algorithms"
    - "parallel minimum cuts in near linear work and low depth"
    - "local flow partitioning for faster edge connectivity"
    - "incremental exact min cut in poly logarithmic amortized update time"
    - "deterministic edge connectivity in near linear time"
    - "distributed minimum cut approximation"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-6 (induced by source paper "weighted min cut sequential cut query and streaming algorithms"):
  with members:
    - "polynomial pass lower bounds for graph streaming algorithms"
    - "a simple algorithm for minimum cuts in near linear time"
    - "local flow partitioning for faster edge connectivity"
    - "computing exact minimum cuts without knowing the graph"
    - "deterministic edge connectivity in near linear time"
    - "single pass spectral sparsification in dynamic streams"
    - "single pass sparsification in the streaming model with edge deletions"
    - "minimum cuts in near linear time"

Connected through hyperedge HE-7 (induced by source paper "minimum cut in o m log 2 n time"):
  with members:
    - "a simple algorithm for minimum cuts in near linear time"
    - "parallel minimum cuts in near linear work and low depth"
    - "local flow partitioning for faster edge connectivity"
    - "computing maximum flow with augmenting electrical flows"
    - "deterministic edge connectivity in near linear time"
    - "maintaining information in fully dynamic trees with top trees"
    - "randomized rounding without solving the linear program"
    - "minimum cuts in near linear time"

Connected through hyperedge HE-8 (induced by source paper "random k out subgraph leaves only o n k inter component edges"):
  with members:
    - "optimal lower bounds for distributed and streaming spanning forest computation"
    - "dynamic graph connectivity with improved worst case update time and sublinear space"

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)