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: "parallel algorithms for geometric graph problems"
  Abstract: We give algorithms for geometric graph problems in the modern parallel models inspired by MapReduce. For example, for the Minimum Spanning Tree (MST) problem over a set of points in the two-dimensional space, our algorithm computes a $(1+\epsilon)$-approximate MST. Our algorithms work in a constant number of rounds of communication, while using total space and communication proportional to the size of the data (linear space and near linear time algorithms). In contrast, for general graphs, achieving the same result for MST (or even connectivity) remains a challenging open problem, despite drawing significant attention in recent years. #R##N#We develop a general algorithmic framework that, besides MST, also applies to Earth-Mover Distance (EMD) and the transportation cost problem. Our algorithmic framework has implications beyond the MapReduce model. For example it yields a new algorithm for computing EMD cost in the plane in near-linear time, $n^{1+o_\epsilon(1)}$. We note that while recently Sharathkumar and Agarwal developed a near-linear time algorithm for $(1+\epsilon)$-approximating EMD, our algorithm is fundamentally different, and, for example, also solves the transportation (cost) problem, raised as an open question in their work. Furthermore, our algorithm immediately gives a $(1+\epsilon)$-approximation algorithm with $n^{\delta}$ space in the streaming-with-sorting model with $1/\delta^{O(1)}$ passes. As such, it is tempting to conjecture that the parallel models may also constitute a concrete playground in the quest for efficient algorithms for EMD (and other similar problems) in the vanilla streaming model, a well-known open problem.

Connected through hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations"):
  with members:
    - "walking randomly massively and efficiently"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "efficient circuit simulation in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "massively parallel algorithms for finding well connected components in sparse graphs"

Connected through hyperedge HE-2 (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"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "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"

Connected through hyperedge HE-3 (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"
    - "faster algorithms for edge connectivity via random 2 out contractions"

Connected through hyperedge HE-4 (induced by source paper "new lower bounds for massively parallel computation from query complexity"):
  with members:
    - "walking randomly massively and efficiently"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "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"
    - "round compression for parallel graph algorithms in strongly sublinear space"

Connected through hyperedge HE-5 (induced by source paper "batch dynamic algorithms via parallel change propagation and applications to dynamic trees"):
  with members:
    - "parallel batch dynamic graph connectivity"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "batch parallel euler tour trees"

Connected through hyperedge HE-6 (induced by source paper "walking randomly massively and efficiently"):
  with members:
    - "exponentially faster massively parallel maximal matching"
    - "simulating random walks on graphs in the streaming model"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "testing graph clusterability algorithms and lower bounds"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "round compression for parallel graph algorithms in strongly sublinear space"

Connected through hyperedge HE-7 (induced by source paper "graph sparsification for derandomizing massively parallel computation with low space"):
  with members:
    - "walking randomly massively and efficiently"
    - "parallel approximate undirected shortest paths via low hop emulators"
    - "deterministic distributed dominating set approximation in the congest model"
    - "efficient deterministic distributed coloring with small bandwidth"
    - "exponentially faster massively parallel maximal matching"
    - "congested clique algorithms for graph spanners"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

Connected through hyperedge HE-8 (induced by source paper "parallel approximate undirected shortest paths via low hop emulators"):
  with members:
    - "preconditioning for the geometric transportation problem"
    - "fast approximate shortest paths in the congested clique"
    - "brief announcement semi mapreduce meets congested clique"
    - "thorup zwick emulators are universally optimal hopsets"
    - "parallel shortest paths using radius stepping"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"

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)