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: "bidirectional heuristic search reconsidered"
  Abstract: The assessment of bidirectional heuristic search has been incorrect since it was first published more than a quarter of a century ago. For quite a long time, this search strategy did not achieve the expected results, and there was a major misunderstanding about the reasons behind it. Although there is still wide-spread belief that bidirectional heuristic search is afflicted by the problem of search frontiers passing each other, we demonstrate that this conjecture is wrong. Based on this finding, we present both a new generic approach to bidirectional heuristic search and a new approach to dynamically improving heuristic values that is feasible in bidirectional search only. These approaches are put into perspective with both the traditional and more recently proposed approaches in order to facilitate a better overall understanding. Empirical results of experiments with our new approaches show that bidirectional heuristic search can be performed very efficiently and also with limited memory. These results suggest that bidirectional heuristic search appears to be better for solving certain difficult problems than corresponding unidirectional search. This provides some evidence for the usefulness of a search strategy that was long neglected. In summary, we show that bidirectional heuristic search is viable and consequently propose that it be reconsidered.

Connected through hyperedge HE-1 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics"):
  with members:
    - "batch informed trees bit informed asymptotically optimal anytime search"
    - "sampling based optimal kinodynamic planning with motion primitives"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "informed sampling for asymptotically optimal path planning"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"

Connected through hyperedge HE-2 (induced by source paper "superintelligence safety a requirements engineering perspective"):
  with members:
    - "modeling agi safety frameworks with causal influence diagrams"
    - "research priorities for robust and beneficial artificial intelligence"
    - "practical verification of decision making in agent based autonomous systems"
    - "revisiting the core ontology and problem in requirements engineering"

Connected through hyperedge HE-3 (induced by source paper "deltapath dataflow based high performance incremental routing"):
  with members:
    - "lying your way to better traffic engineering"
    - "kulfi robust traffic engineering using semi oblivious routing"
    - "lightweight asynchronous snapshots for distributed dataflows"
    - "flexible network bandwidth and latency provisioning in the datacenter"
    - "merlin a language for provisioning network resources"
    - "jellyfish networking data centers randomly"

Connected through hyperedge HE-4 (induced by source paper "kadabra is an adaptive algorithm for betweenness via random approximation"):
  with members:
    - "dynamic merging of frontiers for accelerating the evaluation of betweenness centrality"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "metropolis hastings algorithms for estimating betweenness centrality in large networks"
    - "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "bubing massive crawling for the masses"
    - "fully dynamic approximation of betweenness centrality"
    - "networkit an interactive tool suite for high performance network analysis"

Connected through hyperedge HE-5 (induced by source paper "route planning in transportation networks"):
  with members:
    - "trip based public transit routing"
    - "optimal hub labeling is np complete"
    - "salt a unified framework for all shortest path query variants on road networks"
    - "two phase bicriterion search for finding fast and efficient electric vehicle routes"
    - "customizable contraction hierarchies"
    - "shortest path and distance queries on road networks towards bridging theory and practice"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "distance oracles for time dependent networks"

Connected through hyperedge HE-6 (induced by source paper "anytime heuristic search"):
  with members:
    - "multiple goal heuristic search"
    - "the 3rd international planning competition results and analysis"

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.AI(Artificial Intelligence)