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: "graph pattern detection hardness for all induced patterns and faster non induced cycles"
  Abstract: We consider the pattern detection problem in graphs: given a constant size pattern graph $H$ and a host graph $G$, determine whether $G$ contains a subgraph isomorphic to $H$. Our main results are: #R##N#* We prove that if a pattern $H$ contains a $k$-clique subgraph, then detecting whether an $n$ node host graph contains a not necessarily induced copy of $H$ requires at least the time for detecting whether an $n$ node graph contains a $k$-clique. The previous result of this nature required that $H$ contains a $k$-clique which is disjoint from all other $k$-cliques of $H$. #R##N#* We show that if the famous Hadwiger conjecture from graph theory is true, then detecting whether an $n$ node host graph contains a not necessarily induced copy of a pattern with chromatic number $t$ requires at least the time for detecting whether an $n$ node graph contains a $t$-clique. This implies that: (1) under Hadwiger's conjecture for every $k$-node pattern $H$, finding an induced copy of $H$ requires at least the time of $\sqrt k$-clique detection, and at least size $\omega(n^{\sqrt{k}/4})$ for any constant depth circuit, and (2) unconditionally, detecting an induced copy of a random $G(k,p)$ pattern w.h.p. requires at least the time of $\Theta(k/\log k)$-clique detection, and hence also at least size $n^{\Omega(k/\log k)}$ for circuits of constant depth. #R##N#* Finally, we consider the case when the pattern is a directed cycle on $k$ nodes, and we would like to detect whether a directed $m$-edge graph $G$ contains a $k$-Cycle as a not necessarily induced subgraph. We resolve a 14 year old conjecture of [Yuster-Zwick SODA'04] on the complexity of $k$-Cycle detection by giving a tight analysis of their $k$-Cycle algorithm. Our analysis improves the best bounds for $k$-Cycle detection in directed graphs, for all $k>5$.

Connected through hyperedge HE-1 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs"):
  with members:
    - "decremental sssp in weighted digraphs faster and against an adaptive adversary"
    - "fully dynamic all pairs shortest paths improved worst case time and space bounds"
    - "deterministic algorithms for decremental approximate shortest paths faster and simpler"
    - "decremental strongly connected components and single source reachability in near linear time"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "diameter spanners eccentricity spanners and approximating extremal distances"
    - "limits on the universal method for matrix multiplication"
    - "algorithms and hardness for diameter in dynamic graphs"

Connected through hyperedge HE-2 (induced by source paper "three in a tree in near linear time"):
  with members:
    - "a faster algorithm to recognize even hole free graphs"
    - "on the structure of pan even hole free graphs"
    - "powers of tensors and fast matrix multiplication"
    - "detecting an induced net subdivision"
    - "large induced subgraphs via triangulations and cmso"
    - "induced disjoint paths in claw free graphs"
    - "detecting 2 joins faster"
    - "the k in a path problem for claw free graphs"

Connected through hyperedge HE-3 (induced by source paper "algorithms and hardness for diameter in dynamic graphs"):
  with members:
    - "towards tight approximation bounds for graph diameter and eccentricities"
    - "a note on hardness of diameter approximation"
    - "conditional hardness for sensitivity problems"
    - "near linear lower bounds for distributed distance computations even in sparse networks"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "decremental single source shortest paths on undirected graphs in near linear total update time"
    - "powers of tensors and fast matrix multiplication"
    - "popular conjectures imply strong lower bounds for dynamic problems"

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.CC(Computational Complexity)