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: "detecting multiple communities using quantum annealing on the d wave system"
  Abstract: A very important problem in combinatorial optimization is partitioning a network into communities of densely connected nodes; where the connectivity between nodes inside a particular community is large compared to the connectivity between nodes belonging to different ones. This problem is known as community detection, and has become very important in various fields of science including chemistry, biology and social sciences. The problem of community detection is a twofold problem that consists of determining the number of communities and, at the same time, finding those communities. This drastically increases the solution space for heuristics to work on, compared to traditional graph partitioning problems. In many of the scientific domains in which graphs are used, there is the need to have the ability to partition a graph into communities with the ``highest quality'' possible since the presence of even small isolated communities can become crucial to explain a particular phenomenon. We have explored community detection using the power of quantum annealers, and in particular the D-Wave 2X and 2000Q machines. It turns out that the problem of detecting at most two communities naturally fits into the architecture of a quantum annealer with almost no need of reformulation. This paper addresses a systematic study of detecting two or more communities in a network using a quantum annealer.

Connected through hyperedge HE-1 (induced by source paper "ising based consensus clustering on specialized hardware"):
  with members:
    - "on modeling local search with special purpose combinatorial optimization hardware"

Connected through hyperedge HE-2 (induced by source paper "on modeling local search with special purpose combinatorial optimization hardware"):
  with members:
    - "a tutorial on formulating and using qubo models"
    - "nonnegative binary matrix factorization with a d wave quantum annealer"
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "free energy based reinforcement learning using a quantum processor"
    - "a novel graph based approach for determining molecular similarity"
    - "recent advances in graph partitioning"
    - "advanced coarsening schemes for graph partitioning"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-3 (induced by source paper "performance of a quantum annealer for ising ground state computations on chimera graphs"):
  with members:
    - "a fixed parameter algorithm for the max cut problem on embedded 1 planar graphs"
    - "fast clique minor generation in chimera qubit connectivity graphs"
    - "a ptas for the classical ising spin glass problem on the chimera graph structure"

Connected through hyperedge HE-4 (induced by source paper "a tutorial on formulating and using qubo models"):
  with members:
    - "computing wasserstein distance for persistence diagrams on a quantum computer"
    - "nonnegative binary matrix factorization with a d wave quantum annealer"
    - "logical and inequality implications for reducing the size and complexity of quadratic unconstrained binary optimization problems"
    - "integrating tabu search and vlsn search to develop enhanced algorithms a case study using bipartite boolean quadratic programs"

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.OH(Other Computer Science)