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 the np completeness of some graph cluster measures"
  Abstract: Graph clustering is the problem of identifying sparsely connected dense subgraphs (clusters) in a given graph. Proposed clustering algorithms usually optimize various fitness functions that measure the quality of a cluster within the graph. Examples of such cluster measures include the conductance, the local and relative densities, and single cluster editing. We prove that the decision problems associated with the optimization tasks of finding the clusters that are optimal with respect to these fitness measures are NP-complete.

Connected through hyperedge HE-1 (induced by source paper "a memetic algorithm for the minimum conductance graph partitioning problem"):
  with members:
    - "tunably rugged landscapes with known maximum and minimum"
    - "empirical comparison of algorithms for network community detection"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-2 (induced by source paper "local community detection in dynamic networks"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "empirical comparison of algorithms for network community detection"
    - "finding sparse cuts locally using evolving sets"

Connected through hyperedge HE-3 (induced by source paper "local network community detection with continuous optimization of conductance and weighted kernel k means"):
  with members:
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "heat kernel based community detection"
    - "local graph clustering beyond cheeger s inequality"
    - "defining and evaluating network communities based on ground truth"
    - "empirical comparison of algorithms for network community detection"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "node centric detection of overlapping communities in social networks"):
  with members:
    - "extension of modularity density for overlapping community structure"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "shaping communities out of triangles"
    - "demon a local first discovery method for overlapping communities"
    - "towards linear time overlapping community detection in social networks"
    - "defining and evaluating network communities based on ground truth"
    - "detect overlapping and hierarchical community structure in networks"

Connected through hyperedge HE-5 (induced by source paper "query workload based rdf graph fragmentation and allocation"):
  with members:
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "partout a distributed engine for efficient rdf processing"

Connected through hyperedge HE-6 (induced by source paper "clustering evolving networks"):
  with members:
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "benchmarking community detection methods on social media data"
    - "labelrankt incremental community detection in dynamic networks via label propagation"
    - "on graph stream clustering with side information"
    - "real time discovery of dense clusters in highly dynamic graphs identifying real world events in highly dynamic environments"
    - "detect overlapping and hierarchical community structure in networks"
    - "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "distributed computation of sparse cuts"):
  with members:
    - "fast distributed pagerank computation"
    - "distributed random walks"
    - "fast incremental and personalized pagerank"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"
    - "minimum cuts in near linear time"

Connected through hyperedge HE-8 (induced by source paper "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"):
  with members:
    - "towards an sdp based approach to spectral methods a nearly linear time algorithm for graph partitioning and decomposition"
    - "finding sparse cuts locally using evolving sets"
    - "spectral sparsification of graphs"
    - "a local algorithm for finding dense subgraphs"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

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)