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: "on independent sets in random graphs"
  Abstract: The independence number of a sparse random graph Gn,m of average degree d = 2m/n is well-known to be 2-ednlnd/di¾?αGn,mi¾?2+ednlnd/d with high probability, with edi¾?0 in the limit of large d. Moreover, a trivial greedy algorithm w.h.p. finds an independent set of size nlnd/d, i.e., about half the maximum size. Yet in spite of 30 years of extensive research no efficient algorithm has emerged to produce an independent set with size 1+enlnd/d for any fixede>0 independent of both d and n. In this paper we prove that the combinatorial structure of the independent set problem in random graphs undergoes a phase transition as the size k of the independent sets passes the point k~nlnd/d. Roughly speaking, we prove that independent sets of size k>1+enlnd/d form an intricately rugged landscape, in which local search algorithms seem to get stuck. We illustrate this phenomenon by providing an exponential lower bound for the Metropolis process, a Markov chain for sampling independent sets. © 2014 Wiley Periodicals, Inc. Random Struct. Alg., 47, 436-486, 2015

Connected through hyperedge HE-1 (induced by source paper "the average case complexity of counting cliques in erdos renyi hypergraphs"):
  with members:
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "incoherence optimal matrix completion"
    - "hidden cliques and the certification of the restricted isometry property"
    - "nuclear norm minimization for the planted clique and biclique problems"

Connected through hyperedge HE-2 (induced by source paper "online learning with feedback graphs and switching costs"):
  with members:
    - "unifying the stochastic and the adversarial bandits with knapsack"
    - "bandits with movement costs and adaptive pricing"
    - "multi armed bandits with metric movement costs"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "bandits with switching costs t 2 3 regret"

Connected through hyperedge HE-3 (induced by source paper "constructing concrete hard instances of the maximum independent set problem"):
  with members:
    - "the backtracking survey propagation algorithm for solving random k sat problems"
    - "from average case complexity to improper learning complexity"
    - "average case complexity"
    - "generating hard satisfiable formulas by hiding solutions deceptively"

Connected through hyperedge HE-4 (induced by source paper "inside the clustering window for random linear equations"):
  with members:
    - "the set of solutions of random xorsat formulae"
    - "chasing the k colorability threshold"
    - "catching the k naesat threshold"
    - "frozen variables in random boolean constraint satisfaction problems"
    - "the solution space geometry of random linear equations"
    - "reconstruction and clustering in random constraint satisfaction problems"
    - "tight thresholds for cuckoo hashing via xorsat"
    - "survey propagation an algorithm for satisfiability"

Connected through hyperedge HE-5 (induced by source paper "on percolation and np hardness"):
  with members:
    - "improved hardness of approximating chromatic number"
    - "recoverable values for independent sets"
    - "computational transition at the uniqueness threshold"
    - "subsampling mathematical relaxations and average case complexity"
    - "conditional hardness for approximate coloring"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-6 (induced by source paper "inside the clustering threshold for random linear equations"):
  with members:
    - "chasing the k colorability threshold"
    - "catching the k naesat threshold"
    - "frozen variables in random boolean constraint satisfaction problems"
    - "reconstruction and clustering in random constraint satisfaction problems"
    - "tight thresholds for cuckoo hashing via xorsat"
    - "on the solution space geometry of random constraint satisfaction problems"
    - "a new look at survey propagation and its generalizations"

Connected through hyperedge HE-7 (induced by source paper "frozen variables in random boolean constraint satisfaction problems"):
  with members:
    - "the set of solutions of random xorsat formulae"
    - "catching the k naesat threshold"
    - "the solution space geometry of random linear equations"
    - "on the concentration of the number of solutions of random satisfiability formulas"
    - "reconstruction and clustering in random constraint satisfaction problems"
    - "tight thresholds for cuckoo hashing via xorsat"
    - "decay of correlations for sparse graph error correcting codes"
    - "on the solution space geometry of random constraint satisfaction problems"

Connected through hyperedge HE-8 (induced by source paper "the solution space geometry of random linear equations"):
  with members:
    - "the set of solutions of random xorsat formulae"
    - "the satisfiability threshold for a seemingly intractable random constraint satisfaction problem"
    - "reconstruction and clustering in random constraint satisfaction problems"
    - "tight thresholds for cuckoo hashing via xorsat"

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.DM(Discrete Mathematics)