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: "trade offs between selection complexity and performance when searching the plane without communication"
  Abstract: We consider the ANTS problem [Feinerman et al.] in which a group of agents collaboratively search for a target in a two-dimensional plane. Because this problem is inspired by the behavior of biological species, we argue that in addition to studying the {\em time complexity} of solutions it is also important to study the {\em selection complexity}, a measure of how likely a given algorithmic strategy is to arise in nature due to selective pressures. In more detail, we propose a new selection complexity metric $\chi$, defined for algorithm ${\cal A}$ such that $\chi({\cal A}) = b + \log \ell$, where $b$ is the number of memory bits used by each agent and $\ell$ bounds the fineness of available probabilities (agents use probabilities of at least $1/2^\ell$). In this paper, we study the trade-off between the standard performance metric of speed-up, which measures how the expected time to find the target improves with $n$, and our new selection metric. #R##N#In particular, consider $n$ agents searching for a treasure located at (unknown) distance $D$ from the origin (where $n$ is sub-exponential in $D$). For this problem, we identify $\log \log D$ as a crucial threshold for our selection complexity metric. We first prove a new upper bound that achieves a near-optimal speed-up of $(D^2/n +D) \cdot 2^{O(\ell)}$ for $\chi({\cal A}) \leq 3 \log \log D + O(1)$. In particular, for $\ell \in O(1)$, the speed-up is asymptotically optimal. By comparison, the existing results for this problem [Feinerman et al.] that achieve similar speed-up require $\chi({\cal A}) = \Omega(\log D)$. We then show that this threshold is tight by describing a lower bound showing that if $\chi({\cal A}) < \log \log D - \omega(1)$, then with high probability the target is not found within $D^{2-o(1)}$ moves per agent. Hence, there is a sizable gap to the straightforward $\Omega(D^2/n + D)$ lower bound in this setting.

Connected through hyperedge HE-1 (induced by source paper "lower bounds for shoreline searching with 2 or more robots"):
  with members:
    - "cost vs information tradeoffs for treasure hunt in the plane"
    - "information complexity of treasure hunt in geometric terrains"
    - "reaching a target in the plane with no information"

Connected through hyperedge HE-2 (induced by source paper "evacuating equilateral triangles and squares in the face to face model"):
  with members:
    - "god save the queen"
    - "priority evacuation from a disk using mobile robots"
    - "evacuating robots from a disk using face to face communication"
    - "collaborative search on the plane without communication"

Connected through hyperedge HE-3 (induced by source paper "the ants problem"):
  with members:
    - "parallel linear search with no coordination for a randomly placed treasure"
    - "parallel exhaustive search without coordination"
    - "toward more localized local algorithms removing assumptions concerning global knowledge"
    - "memory lower bounds for randomized collaborative search and applications to biology"
    - "collaborative search on the plane without communication"
    - "beeping a maximal independent set"
    - "physarum can compute shortest paths"
    - "online searching with turn cost"

Connected through hyperedge HE-4 (induced by source paper "parallel linear search with no coordination for a randomly placed treasure"):
  with members:
    - "parallel exhaustive search without coordination"
    - "memory lower bounds for randomized collaborative search and applications to biology"
    - "collaborative search on the plane without communication"
    - "online searching with turn cost"

Connected through hyperedge HE-5 (induced by source paper "the computational power of beeps"):
  with members:
    - "beeping a maximal independent set"
    - "feedback from nature an optimal distributed algorithm for maximal independent set selection"
    - "deploying wireless networks with beeps"
    - "the dynamics of probabilistic population protocols"
    - "the computational power of population protocols"

Connected through hyperedge HE-6 (induced by source paper "parallel exhaustive search without coordination"):
  with members:
    - "memory lower bounds for randomized collaborative search and applications to biology"
    - "collaborative search on the plane without communication"
    - "online searching with turn cost"

Connected through hyperedge HE-7 (induced by source paper "distributed house hunting in ant colonies"):
  with members:
    - "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication"
    - "memory lower bounds for randomized collaborative search and applications to biology"
    - "collaborative search on the plane without communication"

Connected through hyperedge HE-8 (induced by source paper "evacuating robots from a disk using face to face communication"):
  with members:
    - "fast two robot disk evacuation with wireless communication"
    - "search and fetch with 2 robots on a disk wireless and face to face communication models"

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.DC(Distributed, Parallel, and Cluster Computing)