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: "finding even cycles faster via capped k walks"
  Abstract: In this paper, we consider the problem of finding a cycle of length $2k$ (a $C_{2k}$) in an undirected graph $G$ with $n$ nodes and $m$ edges for constant $k\ge2$. A classic result by Bondy and Simonovits [this http URL.'74] implies that if $m \ge100k n^{1+1/k}$, then $G$ contains a $C_{2k}$, further implying that one needs to consider only graphs with $m = O(n^{1+1/k})$. #R##N#Previously the best known algorithms were an $O(n^2)$ algorithm due to Yuster and Zwick [J.Disc.Math'97] as well as a $O(m^{2-(1+\lceil k/2\rceil^{-1})/(k+1)})$ algorithm by Alon et al. [Algorithmica'97]. #R##N#We present an algorithm that uses $O(m^{2k/(k+1)})$ time and finds a $C_{2k}$ if one exists. This bound is $O(n^2)$ exactly when $m=\Theta(n^{1+1/k})$. For $4$-cycles our new bound coincides with Alon et al., while for every $k>2$ our bound yields a polynomial improvement in $m$. #R##N#Yuster and Zwick noted that it is "plausible to conjecture that $O(n^2)$ is the best possible bound in terms of $n$". We show "conditional optimality": if this hypothesis holds then our $O(m^{2k/(k+1)})$ algorithm is tight as well. Furthermore, a folklore reduction implies that no combinatorial algorithm can determine if a graph contains a $6$-cycle in time $O(m^{3/2-\epsilon})$ for any $\epsilon>0$ under the widely believed combinatorial BMM conjecture. Coupled with our main result, this gives tight bounds for finding $6$-cycles combinatorially and also separates the complexity of finding $4$- and $6$-cycles giving evidence that the exponent of $m$ in the running time should indeed increase with $k$. #R##N#The key ingredient in our algorithm is a new notion of capped $k$-walks, which are walks of length $k$ that visit only nodes according to a fixed ordering. Our main technical contribution is an involved analysis proving several properties of such walks which may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "graph pattern detection hardness for all induced patterns and faster non induced cycles"):
  with members:
    - "graph pattern polynomials"
    - "homomorphisms are a good basis for counting small subgraphs"
    - "tight hardness for shortest cycles and paths in sparse graphs"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "clique based lower bounds for parsing tree adjoining grammars"
    - "if the current clique algorithms are optimal so is valiant s parser"
    - "powers of tensors and fast matrix multiplication"
    - "faster algorithms for rectangular matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "improved girth approximation and roundtrip spanners"):
  with members:
    - "nearly work efficient parallel algorithm for digraph reachability"
    - "approximating cycles in directed graphs fast algorithms for girth and roundtrip spanners"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "parallel graph decompositions using random shifts"

Connected through hyperedge HE-3 (induced by source paper "computing quartet distance is equivalent to counting 4 cycles"):
  with members:
    - "on the maximum quartet distance between phylogenetic trees"
    - "powers of tensors and fast matrix multiplication"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "comparing and aggregating partially resolved trees"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-4 (induced by source paper "tight hardness for shortest cycles and paths in sparse graphs"):
  with members:
    - "tree edit distance cannot be computed in strongly subcubic time unless apsp can"
    - "fine grained complexity and conditional hardness for sparse graphs"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "the input output complexity of triangle enumeration"
    - "powers of tensors and fast matrix multiplication"
    - "faster all pairs shortest paths via circuit complexity"
    - "algorithmic applications of baur strassen s theorem shortest cycles diameter and matchings"
    - "all pairs shortest paths using bridging sets and rectangular matrix multiplication"

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)