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: "distributed maximum matching in bounded degree graphs"
  Abstract: We present deterministic distributed algorithms for computing approximate maximum cardinality matchings and approximate maximum weight matchings. Our algorithm for the unweighted case computes a matching whose size is at least $(1-\eps)$ times the optimal in $\Delta^{O(1/\eps)} + O\left(\frac{1}{\eps^2}\right) \cdot\log^*(n)$ rounds where $n$ is the number of vertices in the graph and $\Delta$ is the maximum degree. Our algorithm for the edge-weighted case computes a matching whose weight is at least $(1-\eps)$ times the optimal in $\log(\min\{1/\wmin,n/\eps\})^{O(1/\eps)}\cdot(\Delta^{O(1/\eps)}+\log^*(n))$ rounds for edge-weights in $[\wmin,1]$. #R##N#The best previous algorithms for both the unweighted case and the weighted case are by Lotker, Patt-Shamir, and Pettie~(SPAA 2008). For the unweighted case they give a randomized $(1-\eps)$-approximation algorithm that runs in $O((\log(n)) /\eps^3)$ rounds. For the weighted case they give a randomized $(1/2-\eps)$-approximation algorithm that runs in $O(\log(\eps^{-1}) \cdot \log(n))$ rounds. Hence, our results improve on the previous ones when the parameters $\Delta$, $\eps$ and $\wmin$ are constants (where we reduce the number of runs from $O(\log(n))$ to $O(\log^*(n))$), and more generally when $\Delta$, $1/\eps$ and $1/\wmin$ are sufficiently slowly increasing functions of $n$. Moreover, our algorithms are deterministic rather than randomized.

Connected through hyperedge HE-1 (induced by source paper "distributed maximum matching verification in congest"):
  with members:
    - "lower bounds for maximal matchings and maximal independent sets"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "improved deterministic distributed matching via rounding"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "round compression for parallel matching algorithms"
    - "deterministic distributed edge coloring via hypergraph maximal matching"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "on derandomizing local distributed algorithms"

Connected through hyperedge HE-2 (induced by source paper "distributed approximation algorithms for maximum matching in graphs and hypergraphs"):
  with members:
    - "distributed dense subgraph detection and low outdegree orientation"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "lower bounds for maximal matchings and maximal independent sets"
    - "parameterized distributed algorithms"
    - "improved deterministic distributed matching via rounding"
    - "deterministic distributed edge coloring with fewer colors"
    - "deterministic distributed edge coloring via hypergraph maximal matching"
    - "adapting sequential algorithms to the distributed setting"

Connected through hyperedge HE-3 (induced by source paper "distributed approximation of maximum independent set and maximum matching"):
  with members:
    - "local conflict coloring"
    - "an improved distributed algorithm for maximal independent set"
    - "the locality of distributed symmetry breaking"
    - "approximate graph coloring by semidefinite programming"

Connected through hyperedge HE-4 (induced by source paper "local computation algorithms for graphs of non constant degrees"):
  with members:
    - "flow based algorithms for local graph clustering"
    - "new techniques and tighter bounds for local computation algorithms"
    - "best of two local models local centralized and local distributed algorithms"
    - "local algorithms for sparse spanning graphs"
    - "a local computation approximation scheme to maximum matching"
    - "a simple online competitive adaptation of lempel ziv compression with efficient random access support"
    - "local computation mechanism design"
    - "the power of local information in social networks"

Connected through hyperedge HE-5 (induced by source paper "non local probes do not help with graph problems"):
  with members:
    - "new techniques and tighter bounds for local computation algorithms"
    - "best of two local models local centralized and local distributed algorithms"
    - "local algorithms for sparse spanning graphs"
    - "large cuts with local algorithms on triangle free graphs"
    - "a local computation approximation scheme to maximum matching"
    - "converting online algorithms to local computation algorithms"
    - "local reconstructors and tolerant testers for connectivity and diameter"
    - "fast local computation algorithms"

Connected through hyperedge HE-6 (induced by source paper "new techniques and tighter bounds for local computation algorithms"):
  with members:
    - "local computation algorithms for graphs of non constant degrees"
    - "an improved distributed algorithm for maximal independent set"
    - "a local computation approximation scheme to maximum matching"
    - "local computation mechanism design"
    - "the locality of distributed symmetry breaking"
    - "converting online algorithms to local computation algorithms"
    - "space efficient local computation algorithms"
    - "fast local computation algorithms"

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.DC(Distributed, Parallel, and Cluster Computing)