human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "inside the clustering threshold for random linear equations"):
  Abstract: We study a random system of $cn$ linear equations over $n$ variables in GF(2), where each equation contains exactly $r$ variables; this is equivalent to $r$-XORSAT. \cite{ikkm,amxor} determined the clustering threshold, $c^*_r$: if $c=c^*_r+\e$ for any constant $\e>0$, then \aas the solutions partition into well-connected, well-separated {\em clusters} (with probability tending to 1 as $n\rightarrow\infty$). This is part of a general clustering phenomenon which is hypothesized to arise in most of the commonly studied models of random constraint satisfaction problems, via sophisticated but mostly non-rigorous techniques from statistical physics. We extend that study to the range $c=c^*_r+o(1)$, showing that if $c=c^*_r+n^{-\d}, \d>0$, then the connectivity parameter of each $r$-XORSAT cluster is $n^{\Theta(\d)}$, as compared to $O(\log n)$ when $c=c^*_r+\e$. This means that one can move between any two solutions in the same cluster via a sequence of solutions where consecutive solutions differ on at most $n^{\Theta(\d)}$ variables; this is tight up to the implicit constant. In contrast, moving to a solution in another cluster requires that some pair of consecutive solutions differ in at least $n^{1-O(\d)}$ variables. #R##N#Along the way, we prove that in a random $r$-uniform hypergraph with edge-density $n^{-\d}$ above the $k$-core threshold, \aas every vertex not in the $k$-core can be removed by a sequence of $n^{\Theta(\d)}$ vertex-deletions in which the deleted vertex has degree less than $k$; again, this is tight up to the implicit constant.

Includes members:
  Member "on independent sets in random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the average case complexity of counting cliques in erdos renyi hypergraphs")
  Member "chasing the k colorability threshold":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "information theoretic thresholds from the cavity method")
  Member "catching the k naesat threshold":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "general bounds on satisfiability thresholds for random csps via fourier analysis")
  Member "frozen variables in random boolean constraint satisfaction problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "planting colourings silently")
  Member "reconstruction and clustering in random constraint satisfaction problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "broadcasting on random directed acyclic graphs")
  Member "tight thresholds for cuckoo hashing via xorsat":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures")
  Member "on the solution space geometry of random constraint satisfaction problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quantitative verification of neural networks and its security applications")
  Member "a new look at survey propagation and its generalizations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "streamlining variational inference for constraint satisfaction problems")

Question: Please tell me which class the source paper 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.DM(Discrete Mathematics)